Pandas Profiling Report

Dataset statistics

Number of variables	33
Number of observations	13815
Missing cells	58818
Missing cells (%)	12.9%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	4.1 MiB
Average record size in memory	310.3 B

Variable types

Categorical	11
Numeric	19
Unsupported	2
Boolean	1

Alerts

`name` has a high cardinality: 13205 distinct values	High cardinality
`host_name` has a high cardinality: 3792 distinct values	High cardinality
`last_review` has a high cardinality: 1243 distinct values	High cardinality
`property_type` has a high cardinality: 52 distinct values	High cardinality
`first_review` has a high cardinality: 2708 distinct values	High cardinality
`listing_url` has a high cardinality: 13815 distinct values	High cardinality
`host_about` has a high cardinality: 5856 distinct values	High cardinality
`host_response_rate` has a high cardinality: 63 distinct values	High cardinality
`longitude` is highly overall correlated with `neighbourhood`	High correlation
`price` is highly overall correlated with `accommodates`	High correlation
`number_of_reviews` is highly overall correlated with `number_of_reviews_ltm`	High correlation
`reviews_per_month` is highly overall correlated with `number_of_reviews_ltm`	High correlation
`calculated_host_listings_count` is highly overall correlated with `host_response_rate`	High correlation
`number_of_reviews_ltm` is highly overall correlated with `number_of_reviews` and 1 other fields	High correlation
`accommodates` is highly overall correlated with `price`	High correlation
`review_scores_value` is highly overall correlated with `review_scores_accuracy` and 1 other fields	High correlation
`review_scores_cleanliness` is highly overall correlated with `review_scores_accuracy` and 1 other fields	High correlation
`review_scores_accuracy` is highly overall correlated with `review_scores_value` and 4 other fields	High correlation
`review_scores_communication` is highly overall correlated with `review_scores_accuracy` and 2 other fields	High correlation
`review_scores_checkin` is highly overall correlated with `review_scores_accuracy` and 1 other fields	High correlation
`review_scores_rating` is highly overall correlated with `review_scores_value` and 3 other fields	High correlation
`neighbourhood` is highly overall correlated with `longitude`	High correlation
`room_type` is highly overall correlated with `property_type`	High correlation
`property_type` is highly overall correlated with `room_type`	High correlation
`host_response_time` is highly overall correlated with `host_response_rate`	High correlation
`host_response_rate` is highly overall correlated with `calculated_host_listings_count` and 1 other fields	High correlation
`room_type` is highly imbalanced (73.3%)	Imbalance
`property_type` is highly imbalanced (57.9%)	Imbalance
`host_is_superhost` is highly imbalanced (50.4%)	Imbalance
`host_response_rate` is highly imbalanced (69.1%)	Imbalance
`neighbourhood_group` has 13815 (100.0%) missing values	Missing
`last_review` has 1645 (11.9%) missing values	Missing
`reviews_per_month` has 1645 (11.9%) missing values	Missing
`license` has 13815 (100.0%) missing values	Missing
`first_review` has 1645 (11.9%) missing values	Missing
`review_scores_value` has 1688 (12.2%) missing values	Missing
`review_scores_cleanliness` has 1688 (12.2%) missing values	Missing
`review_scores_location` has 1689 (12.2%) missing values	Missing
`review_scores_accuracy` has 1688 (12.2%) missing values	Missing
`review_scores_communication` has 1688 (12.2%) missing values	Missing
`review_scores_checkin` has 1688 (12.2%) missing values	Missing
`review_scores_rating` has 1645 (11.9%) missing values	Missing
`host_about` has 6970 (50.5%) missing values	Missing
`host_response_time` has 3753 (27.2%) missing values	Missing
`host_response_rate` has 3753 (27.2%) missing values	Missing
`price` is highly skewed (γ1 = 22.74963003)	Skewed
`minimum_nights` is highly skewed (γ1 = 33.10781304)	Skewed
`name` is uniformly distributed	Uniform
`listing_url` is uniformly distributed	Uniform
`listing_url` has unique values	Unique
`neighbourhood_group` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`license` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`number_of_reviews` has 1645 (11.9%) zeros	Zeros
`availability_365` has 4846 (35.1%) zeros	Zeros
`number_of_reviews_ltm` has 3898 (28.2%) zeros	Zeros

Reproduction

Analysis started	2023-01-21 14:07:26.145317
Analysis finished	2023-01-21 14:08:34.197183
Duration	1 minute and 8.05 seconds
Software version	pandas-profiling vv3.6.2
Download configuration	config.json

name
Categorical

HIGH CARDINALITY UNIFORM

Distinct	13205
Distinct (%)	95.6%
Missing	0
Missing (%)	0.0%
Memory size	215.9 KiB

Cozy apartment in the heart of Copenhagen	21
Cozy apartment in the heart of Nørrebro	19
Skøn lejlighed i København	19
Charming apartment in the heart of Copenhagen	14
Cozy apartment in Copenhagen	14
Other values (13200)	13728

Length

Max length	127
Median length	107
Mean length	40.617879
Min length	1

Characters and Unicode

Total characters	561136
Distinct characters	182
Distinct categories	20 ?
Distinct scripts	5 ?
Distinct blocks	12 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	12909 ?
Unique (%)	93.4%

Sample

1st row	Copenhagen 'N Livin'
2nd row	Lovely house - most attractive area
3rd row	City Centre Townhouse Sleeps 1-10 persons
4th row	Best Location in Cool Istedgade
5th row	Beautiful, spacious, central, renovated Penthouse

Common Values

Value	Count	Frequency (%)
Cozy apartment in the heart of Copenhagen	21	0.2%
Cozy apartment in the heart of Nørrebro	19	0.1%
Skøn lejlighed i København	19	0.1%
Charming apartment in the heart of Copenhagen	14	0.1%
Cozy apartment in Copenhagen	14	0.1%
Lovely apartment in the heart of Copenhagen	13	0.1%
Skøn lejlighed med gårdhave	13	0.1%
Spacious apartment in the heart of Copenhagen	12	0.1%
Skøn lejlighed på Frederiksberg	11	0.1%
Charming apartment in Copenhagen	10	0.1%
Other values (13195)	13669	98.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
apartment	5215	5.8%
in	4746	5.3%
the	2433	2.7%
copenhagen	2301	2.6%
	2230	2.5%
and	1820	2.0%
lejlighed	1767	2.0%
with	1734	1.9%
of	1567	1.8%
to	1454	1.6%
Other values (4414)	64136	71.7%

Most occurring characters

Value	Count	Frequency (%)
	75787	13.5%
e	55164	9.8%
t	39563	7.1%
a	37584	6.7%
r	36121	6.4%
n	36004	6.4%
o	31618	5.6%
i	26844	4.8%
l	20458	3.6%
h	17174	3.1%
Other values (172)	184819	32.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	435885	77.7%
Space Separator	75802	13.5%
Uppercase Letter	32876	5.9%
Other Punctuation	6520	1.2%
Decimal Number	6062	1.1%
Dash Punctuation	2538	0.5%
Close Punctuation	391	0.1%
Open Punctuation	344	0.1%
Other Symbol	268	< 0.1%
Math Symbol	248	< 0.1%
Other values (10)	202	< 0.1%

Most frequent character per category

Other Symbol

Value	Count	Frequency (%)
★	181	67.5%
❤	18	6.7%
☀	12	4.5%
♥	9	3.4%
☆	4	1.5%
🌞	3	1.1%
❀	3	1.1%
🌟	2	0.7%
🌻	2	0.7%
❣	2	0.7%
Other values (29)	32	11.9%

Lowercase Letter

Value	Count	Frequency (%)
e	55164	12.7%
t	39563	9.1%
a	37584	8.6%
r	36121	8.3%
n	36004	8.3%
o	31618	7.3%
i	26844	6.2%
l	20458	4.7%
h	17174	3.9%
m	14829	3.4%
Other values (27)	120526	27.7%

Uppercase Letter

Value	Count	Frequency (%)
C	7788	23.7%
S	2525	7.7%
N	2435	7.4%
B	2103	6.4%
L	2077	6.3%
A	1895	5.8%
H	1812	5.5%
P	1629	5.0%
V	1325	4.0%
F	1278	3.9%
Other values (19)	8009	24.4%

Other Letter

Value	Count	Frequency (%)
房	2	10.5%
客	2	10.5%
ら	1	5.3%
か	1	5.3%
街	1	5.3%
分	1	5.3%
伝	1	5.3%
統	1	5.3%
的	1	5.3%
な	1	5.3%
Other values (7)	7	36.8%

Other Punctuation

Value	Count	Frequency (%)
,	2428	37.2%
.	2028	31.1%
&	623	9.6%
!	544	8.3%
/	421	6.5%
"	116	1.8%
'	108	1.7%
:	93	1.4%
·	72	1.1%
*	37	0.6%
Other values (6)	50	0.8%

Decimal Number

Value	Count	Frequency (%)
2	1681	27.7%
1	1401	23.1%
0	797	13.1%
3	643	10.6%
5	484	8.0%
4	375	6.2%
6	206	3.4%
7	182	3.0%
8	162	2.7%
9	131	2.2%

Math Symbol

Value	Count	Frequency (%)
\|	130	52.4%
+	97	39.1%
<	17	6.9%
~	2	0.8%
=	1	0.4%
>	1	0.4%

Modifier Symbol

Value	Count	Frequency (%)
´	5	45.5%
^	4	36.4%
`	1	9.1%
🏼	1	9.1%

Dash Punctuation

Value	Count	Frequency (%)
-	2524	99.4%
–	12	0.5%
—	2	0.1%

Close Punctuation

Value	Count	Frequency (%)
)	381	97.4%
]	9	2.3%
）	1	0.3%

Final Punctuation

Value	Count	Frequency (%)
’	45	75.0%
”	14	23.3%
»	1	1.7%

Initial Punctuation

Value	Count	Frequency (%)
“	14	66.7%
‘	6	28.6%
«	1	4.8%

Format

Value	Count	Frequency (%)
	2	50.0%
‍	1	25.0%
‭	1	25.0%

Space Separator

Value	Count	Frequency (%)
	75787	> 99.9%
	15	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
(	335	97.4%
[	9	2.6%

Control

Value	Count	Frequency (%)
	45	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	33	100.0%

Other Number

Value	Count	Frequency (%)
²	6	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	2	100.0%

Currency Symbol

Value	Count	Frequency (%)
€	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	468761	83.5%
Common	92322	16.5%
Inherited	34	< 0.1%
Han	14	< 0.1%
Hiragana	5	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	75787	82.1%
-	2524	2.7%
,	2428	2.6%
.	2028	2.2%
2	1681	1.8%
1	1401	1.5%
0	797	0.9%
3	643	0.7%
&	623	0.7%
!	544	0.6%
Other values (87)	3866	4.2%

Latin

Value	Count	Frequency (%)
e	55164	11.8%
t	39563	8.4%
a	37584	8.0%
r	36121	7.7%
n	36004	7.7%
o	31618	6.7%
i	26844	5.7%
l	20458	4.4%
h	17174	3.7%
m	14829	3.2%
Other values (56)	153402	32.7%

Han

Value	Count	Frequency (%)
房	2	14.3%
客	2	14.3%
街	1	7.1%
分	1	7.1%
伝	1	7.1%
統	1	7.1%
的	1	7.1%
佇	1	7.1%
馨	1	7.1%
温	1	7.1%
Other values (2)	2	14.3%

Hiragana

Value	Count	Frequency (%)
ら	1	20.0%
か	1	20.0%
な	1	20.0%
ま	1	20.0%
い	1	20.0%

Inherited

Value	Count	Frequency (%)
️	33	97.1%
‍	1	2.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	555066	98.9%
None	5672	1.0%
Misc Symbols	209	< 0.1%
Punctuation	100	< 0.1%
VS	33	< 0.1%
Dingbats	27	< 0.1%
CJK	14	< 0.1%
Emoticons	6	< 0.1%
Hiragana	5	< 0.1%
Enclosed Alphanum Sup	2	< 0.1%
Other values (2)	2	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	75787	13.7%
e	55164	9.9%
t	39563	7.1%
a	37584	6.8%
r	36121	6.5%
n	36004	6.5%
o	31618	5.7%
i	26844	4.8%
l	20458	3.7%
h	17174	3.1%
Other values (82)	178749	32.2%

None

Value	Count	Frequency (%)
ø	2785	49.1%
å	1210	21.3%
æ	1084	19.1%
Ø	440	7.8%
·	72	1.3%
	15	0.3%
é	8	0.1%
²	6	0.1%
Å	5	0.1%
´	5	0.1%
Other values (31)	42	0.7%

Misc Symbols

Value	Count	Frequency (%)
★	181	86.6%
☀	12	5.7%
♥	9	4.3%
☆	4	1.9%
♀	1	0.5%
☺	1	0.5%
⛵	1	0.5%

Punctuation

Value	Count	Frequency (%)
’	45	45.0%
”	14	14.0%
“	14	14.0%
–	12	12.0%
‘	6	6.0%
•	5	5.0%
—	2	2.0%
‍	1	1.0%
‭	1	1.0%

VS

Value	Count	Frequency (%)
️	33	100.0%

Dingbats

Value	Count	Frequency (%)
❤	18	66.7%
❀	3	11.1%
❣	2	7.4%
✨	2	7.4%
✈	1	3.7%
➡	1	3.7%

CJK

Value	Count	Frequency (%)
房	2	14.3%
客	2	14.3%
街	1	7.1%
分	1	7.1%
伝	1	7.1%
統	1	7.1%
的	1	7.1%
佇	1	7.1%
馨	1	7.1%
温	1	7.1%
Other values (2)	2	14.3%

Emoticons

Value	Count	Frequency (%)
😊	2	33.3%
😻	1	16.7%
🙂	1	16.7%
🙏	1	16.7%
😍	1	16.7%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇰	1	50.0%
🇩	1	50.0%

Hiragana

Value	Count	Frequency (%)
ら	1	20.0%
か	1	20.0%
な	1	20.0%
ま	1	20.0%
い	1	20.0%

Enclosed Alphanum

Value	Count	Frequency (%)
Ⓡ	1	100.0%

Currency Symbols

Value	Count	Frequency (%)
€	1	100.0%

host_id
Real number (ℝ)

Distinct	12437
Distinct (%)	90.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.064145 × 10⁸

Minimum	11718
Maximum	4.8058027 × 10⁸
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	11718
5-th percentile	2370627.4
Q1	13563826
median	46563012
Q3	1.5534864 × 10⁸
95-th percentile	4.2793561 × 10⁸
Maximum	4.8058027 × 10⁸
Range	4.8056855 × 10⁸
Interquartile range (IQR)	1.4178481 × 10⁸

Descriptive statistics

Standard deviation	1.3028012 × 10⁸
Coefficient of variation (CV)	1.2242704
Kurtosis	1.1312881
Mean	1.064145 × 10⁸
Median Absolute Deviation (MAD)	40510540
Skewness	1.4827853
Sum	1.4701163 × 10¹²
Variance	1.697291 × 10¹⁶
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
187610263	179	1.3%
155348640	82	0.6%
437864658	55	0.4%
424093643	26	0.2%
179753578	24	0.2%
461726397	17	0.1%
449069915	16	0.1%
3366007	13	0.1%
244829847	13	0.1%
2806924	12	0.1%
Other values (12427)	13378	96.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
11718	1	< 0.1%
12155	1	< 0.1%
16774	1	< 0.1%
18939	1	< 0.1%
18968	1	< 0.1%
21375	1	< 0.1%
33238	1	< 0.1%
33741	1	< 0.1%
45243	1	< 0.1%
49071	1	< 0.1%

Value	Count	Frequency (%)
480580266	1	< 0.1%
480482332	1	< 0.1%
480322216	1	< 0.1%
480291576	1	< 0.1%
480193317	1	< 0.1%
480156533	1	< 0.1%
479904066	1	< 0.1%
479856647	1	< 0.1%
479715426	1	< 0.1%
479597843	1	< 0.1%

host_name
Categorical

Distinct	3792
Distinct (%)	27.5%
Missing	1
Missing (%)	< 0.1%
Memory size	215.9 KiB

ApartmentinCopenhagen	179
Christian	145
Mette	144
Maria	125
Julie	120
Other values (3787)	13101

Length

Max length	31
Median length	27
Mean length	7.0167946
Min length	1

Characters and Unicode

Total characters	96930
Distinct characters	99
Distinct categories	9 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2749 ?
Unique (%)	19.9%

Sample

1st row	Simon
2nd row	Kari
3rd row	Julia
4th row	Nana
5th row	Ebbe

Common Values

Value	Count	Frequency (%)
ApartmentinCopenhagen	179	1.3%
Christian	145	1.0%
Mette	144	1.0%
Maria	125	0.9%
Julie	120	0.9%
Anna	113	0.8%
Rasmus	112	0.8%
Mads	106	0.8%
Thomas	106	0.8%
Louise	104	0.8%
Other values (3782)	12560	90.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
maria	181	1.1%
apartmentincopenhagen	179	1.1%
christian	179	1.1%
mette	178	1.1%
anne	172	1.1%
	170	1.1%
julie	149	0.9%
marie	149	0.9%
anna	140	0.9%
sofie	137	0.8%
Other values (3226)	14527	89.9%

Most occurring characters

Value	Count	Frequency (%)
e	11246	11.6%
a	10928	11.3%
i	8898	9.2%
n	8626	8.9%
r	6324	6.5%
t	4500	4.6%
l	4316	4.5%
s	4129	4.3%
o	3534	3.6%
	2354	2.4%
Other values (89)	32075	33.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	77880	80.3%
Uppercase Letter	16326	16.8%
Space Separator	2354	2.4%
Other Punctuation	211	0.2%
Dash Punctuation	122	0.1%
Close Punctuation	10	< 0.1%
Open Punctuation	10	< 0.1%
Decimal Number	10	< 0.1%
Math Symbol	7	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	11246	14.4%
a	10928	14.0%
i	8898	11.4%
n	8626	11.1%
r	6324	8.1%
t	4500	5.8%
l	4316	5.5%
s	4129	5.3%
o	3534	4.5%
h	2270	2.9%
Other values (43)	13109	16.8%

Uppercase Letter

Value	Count	Frequency (%)
M	2117	13.0%
A	1783	10.9%
S	1640	10.0%
C	1372	8.4%
J	1279	7.8%
L	1069	6.5%
K	893	5.5%
T	735	4.5%
N	621	3.8%
R	588	3.6%
Other values (22)	4229	25.9%

Decimal Number

Value	Count	Frequency (%)
5	6	60.0%
3	1	10.0%
6	1	10.0%
1	1	10.0%
7	1	10.0%

Other Punctuation

Value	Count	Frequency (%)
&	158	74.9%
.	47	22.3%
/	3	1.4%
,	3	1.4%

Space Separator

Value	Count	Frequency (%)
	2354	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	122	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	10	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	10	100.0%

Math Symbol

Value	Count	Frequency (%)
+	7	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	94206	97.2%
Common	2724	2.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	11246	11.9%
a	10928	11.6%
i	8898	9.4%
n	8626	9.2%
r	6324	6.7%
t	4500	4.8%
l	4316	4.6%
s	4129	4.4%
o	3534	3.8%
h	2270	2.4%
Other values (75)	29435	31.2%

Common

Value	Count	Frequency (%)
	2354	86.4%
&	158	5.8%
-	122	4.5%
.	47	1.7%
)	10	0.4%
(	10	0.4%
+	7	0.3%
5	6	0.2%
/	3	0.1%
,	3	0.1%
Other values (4)	4	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	96555	99.6%
None	375	0.4%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	11246	11.6%
a	10928	11.3%
i	8898	9.2%
n	8626	8.9%
r	6324	6.5%
t	4500	4.7%
l	4316	4.5%
s	4129	4.3%
o	3534	3.7%
	2354	2.4%
Other values (56)	31700	32.8%

None

Value	Count	Frequency (%)
ø	205	54.7%
æ	52	13.9%
é	30	8.0%
Ø	14	3.7%
å	10	2.7%
ö	9	2.4%
ó	7	1.9%
ü	7	1.9%
ð	5	1.3%
í	5	1.3%
Other values (23)	31	8.3%

neighbourhood_group
Unsupported

MISSING REJECTED UNSUPPORTED

Missing	13815
Missing (%)	100.0%
Memory size	215.9 KiB

neighbourhood
Categorical

Distinct	11
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	215.9 KiB

Vesterbro-Kongens Enghave	2290
Nrrebro	2268
Indre By	2068
sterbro	1511
Frederiksberg	1367
Other values (6)	4311

Length

Max length	25
Median length	12
Mean length	11.386174
Min length	5

Characters and Unicode

Total characters	157300
Distinct characters	31
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Nrrebro
2nd row	Indre By
3rd row	Indre By
4th row	Vesterbro-Kongens Enghave
5th row	Vesterbro-Kongens Enghave

Common Values

Value	Count	Frequency (%)
Vesterbro-Kongens Enghave	2290	16.6%
Nrrebro	2268	16.4%
Indre By	2068	15.0%
sterbro	1511	10.9%
Frederiksberg	1367	9.9%
Amager Vest	1235	8.9%
Amager st	1026	7.4%
Bispebjerg	698	5.1%
Valby	663	4.8%
Vanlse	350	2.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
vesterbro-kongens	2290	11.2%
enghave	2290	11.2%
nrrebro	2268	11.1%
amager	2261	11.1%
indre	2068	10.1%
by	2068	10.1%
sterbro	1511	7.4%
frederiksberg	1367	6.7%
vest	1235	6.0%
st	1026	5.0%
Other values (4)	2050	10.0%

Most occurring characters

Value	Count	Frequency (%)
e	24350	15.5%
r	23873	15.2%
s	11445	7.3%
n	9627	6.1%
g	8906	5.7%
b	8797	5.6%
o	8359	5.3%
	6619	4.2%
t	6062	3.9%
a	5564	3.5%
Other values (21)	43698	27.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	127526	81.1%
Uppercase Letter	20526	13.0%
Space Separator	6619	4.2%
Dash Punctuation	2629	1.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	24350	19.1%
r	23873	18.7%
s	11445	9.0%
n	9627	7.5%
g	8906	7.0%
b	8797	6.9%
o	8359	6.6%
t	6062	4.8%
a	5564	4.4%
d	3435	2.7%
Other values (10)	17108	13.4%

Uppercase Letter

Value	Count	Frequency (%)
V	4538	22.1%
B	3105	15.1%
E	2290	11.2%
K	2290	11.2%
N	2268	11.0%
A	2261	11.0%
I	2068	10.1%
F	1367	6.7%
H	339	1.7%

Space Separator

Value	Count	Frequency (%)
	6619	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2629	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	148052	94.1%
Common	9248	5.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	24350	16.4%
r	23873	16.1%
s	11445	7.7%
n	9627	6.5%
g	8906	6.0%
b	8797	5.9%
o	8359	5.6%
t	6062	4.1%
a	5564	3.8%
V	4538	3.1%
Other values (19)	36531	24.7%

Common

Value	Count	Frequency (%)
	6619	71.6%
-	2629	28.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	157300	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	24350	15.5%
r	23873	15.2%
s	11445	7.3%
n	9627	6.1%
g	8906	5.7%
b	8797	5.6%
o	8359	5.3%
	6619	4.2%
t	6062	3.9%
a	5564	3.5%
Other values (21)	43698	27.8%

latitude
Real number (ℝ)

Distinct	8432
Distinct (%)	61.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	55.680569

Minimum	55.60951
Maximum	55.7428
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	55.60951
5-th percentile	55.646247
Q1	55.66611
median	55.68119
Q3	55.69577
95-th percentile	55.711329
Maximum	55.7428
Range	0.13329
Interquartile range (IQR)	0.02966

Descriptive statistics

Standard deviation	0.021088474
Coefficient of variation (CV)	0.00037874027
Kurtosis	0.12564487
Mean	55.680569
Median Absolute Deviation (MAD)	0.0149507
Skewness	-0.066060023
Sum	769227.06
Variance	0.00044472372
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
55.66658	9	0.1%
55.66538	9	0.1%
55.66005	9	0.1%
55.66926	8	0.1%
55.67023	8	0.1%
55.66714	8	0.1%
55.66583	8	0.1%
55.67714	8	0.1%
55.66619	7	0.1%
55.69999	7	0.1%
Other values (8422)	13734	99.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
55.60951	1	< 0.1%
55.61186	1	< 0.1%
55.6121384	1	< 0.1%
55.613216	1	< 0.1%
55.61336	1	< 0.1%
55.61343	1	< 0.1%
55.61356071	1	< 0.1%
55.61499	1	< 0.1%
55.61519	1	< 0.1%
55.615391	1	< 0.1%

Value	Count	Frequency (%)
55.7428	1	< 0.1%
55.74273	1	< 0.1%
55.7427	1	< 0.1%
55.74267	1	< 0.1%
55.74265	1	< 0.1%
55.74258	1	< 0.1%
55.74249	1	< 0.1%
55.74246	1	< 0.1%
55.74243	1	< 0.1%
55.74241	1	< 0.1%

longitude
Real number (ℝ)

Distinct	9663
Distinct (%)	69.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	12.557805

Minimum	12.43567
Maximum	12.65174
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	12.43567
5-th percentile	12.494894
Q1	12.540285
median	12.55552
Q3	12.580741
95-th percentile	12.614333
Maximum	12.65174
Range	0.21607
Interquartile range (IQR)	0.040455522

Descriptive statistics

Standard deviation	0.033511545
Coefficient of variation (CV)	0.0026685829
Kurtosis	0.40370677
Mean	12.557805
Median Absolute Deviation (MAD)	0.020459
Skewness	-0.29914072
Sum	173486.08
Variance	0.0011230236
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
12.55347	9	0.1%
12.54652	8	0.1%
12.54526	7	0.1%
12.54301	7	0.1%
12.54948	7	0.1%
12.57523	7	0.1%
12.55351	6	< 0.1%
12.55167	6	< 0.1%
12.5466	6	< 0.1%
12.54637	6	< 0.1%
Other values (9653)	13746	99.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
12.43567	1	< 0.1%
12.43769	1	< 0.1%
12.43788	1	< 0.1%
12.43817	1	< 0.1%
12.43831	1	< 0.1%
12.43909	1	< 0.1%
12.43911	1	< 0.1%
12.43952	1	< 0.1%
12.44034	1	< 0.1%
12.44062	1	< 0.1%

Value	Count	Frequency (%)
12.65174	1	< 0.1%
12.6491	1	< 0.1%
12.64645	1	< 0.1%
12.64593	1	< 0.1%
12.64571	1	< 0.1%
12.64545	1	< 0.1%
12.64498	1	< 0.1%
12.64495	1	< 0.1%
12.6446	1	< 0.1%
12.64399	1	< 0.1%

room_type
Categorical

HIGH CORRELATION IMBALANCE

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	215.9 KiB

Entire home/apt	12230
Private room	1551
Shared room	19
Hotel room	15

Length

Max length	15
Median length	15
Mean length	14.652262
Min length	10

Characters and Unicode

Total characters	202421
Distinct characters	19
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Entire home/apt
2nd row	Entire home/apt
3rd row	Entire home/apt
4th row	Entire home/apt
5th row	Entire home/apt

Common Values

Value	Count	Frequency (%)
Entire home/apt	12230	88.5%
Private room	1551	11.2%
Shared room	19	0.1%
Hotel room	15	0.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
entire	12230	44.3%
home/apt	12230	44.3%
room	1585	5.7%
private	1551	5.6%
shared	19	0.1%
hotel	15	0.1%

Most occurring characters

Value	Count	Frequency (%)
e	26045	12.9%
t	26026	12.9%
o	15415	7.6%
r	15385	7.6%
m	13815	6.8%
	13815	6.8%
a	13800	6.8%
i	13781	6.8%
h	12249	6.1%
p	12230	6.0%
Other values (9)	39860	19.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	162561	80.3%
Space Separator	13815	6.8%
Uppercase Letter	13815	6.8%
Other Punctuation	12230	6.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	26045	16.0%
t	26026	16.0%
o	15415	9.5%
r	15385	9.5%
m	13815	8.5%
a	13800	8.5%
i	13781	8.5%
h	12249	7.5%
p	12230	7.5%
n	12230	7.5%
Other values (3)	1585	1.0%

Uppercase Letter

Value	Count	Frequency (%)
E	12230	88.5%
P	1551	11.2%
S	19	0.1%
H	15	0.1%

Space Separator

Value	Count	Frequency (%)
	13815	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	12230	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	176376	87.1%
Common	26045	12.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	26045	14.8%
t	26026	14.8%
o	15415	8.7%
r	15385	8.7%
m	13815	7.8%
a	13800	7.8%
i	13781	7.8%
h	12249	6.9%
p	12230	6.9%
E	12230	6.9%
Other values (7)	15400	8.7%

Common

Value	Count	Frequency (%)
	13815	53.0%
/	12230	47.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	202421	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	26045	12.9%
t	26026	12.9%
o	15415	7.6%
r	15385	7.6%
m	13815	6.8%
	13815	6.8%
a	13800	6.8%
i	13781	6.8%
h	12249	6.1%
p	12230	6.0%
Other values (9)	39860	19.7%

price
Real number (ℝ)

HIGH CORRELATION SKEWED

Distinct	1689
Distinct (%)	12.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1205.879

Minimum	0
Maximum	64900
Zeros	2
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	400.7
Q1	729
median	995
Q3	1368.5
95-th percentile	2483.6
Maximum	64900
Range	64900
Interquartile range (IQR)	639.5

Descriptive statistics

Standard deviation	1433.1431
Coefficient of variation (CV)	1.1884635
Kurtosis	835.22293
Mean	1205.879
Median Absolute Deviation (MAD)	295
Skewness	22.74963
Sum	16659218
Variance	2053899.3
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1000	656	4.7%
1200	563	4.1%
800	506	3.7%
900	439	3.2%
1500	425	3.1%
700	364	2.6%
750	360	2.6%
1100	320	2.3%
850	312	2.3%
950	284	2.1%
Other values (1679)	9586	69.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	2	< 0.1%
79	1	< 0.1%
113	1	< 0.1%
120	1	< 0.1%
124	1	< 0.1%
127	1	< 0.1%
134	1	< 0.1%
135	1	< 0.1%
143	1	< 0.1%
145	1	< 0.1%

Value	Count	Frequency (%)
64900	1	< 0.1%
62000	1	< 0.1%
58000	1	< 0.1%
44000	1	< 0.1%
41800	1	< 0.1%
36200	1	< 0.1%
20000	2	< 0.1%
17857	1	< 0.1%
15000	1	< 0.1%
14000	1	< 0.1%

minimum_nights
Real number (ℝ)

Distinct	69
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.6055737

Minimum	1
Maximum	1111
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	4
95-th percentile	8
Maximum	1111
Range	1110
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	16.900153
Coefficient of variation (CV)	3.6695
Kurtosis	1650.7366
Mean	4.6055737
Median Absolute Deviation (MAD)	1
Skewness	33.107813
Sum	63626
Variance	285.61516
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	3641	26.4%
3	3140	22.7%
1	2283	16.5%
4	1698	12.3%
5	1352	9.8%
7	550	4.0%
6	436	3.2%
14	149	1.1%
10	109	0.8%
30	107	0.8%
Other values (59)	350	2.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	2283	16.5%
2	3641	26.4%
3	3140	22.7%
4	1698	12.3%
5	1352	9.8%
6	436	3.2%
7	550	4.0%
8	30	0.2%
9	13	0.1%
10	109	0.8%

Value	Count	Frequency (%)
1111	1	< 0.1%
600	1	< 0.1%
500	2	< 0.1%
400	1	< 0.1%
365	1	< 0.1%
360	2	< 0.1%
350	1	< 0.1%
300	2	< 0.1%
210	1	< 0.1%
200	3	< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	266
Distinct (%)	1.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.571118

Minimum	0
Maximum	711
Zeros	1645
Zeros (%)	11.9%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	7
Q3	20
95-th percentile	73
Maximum	711
Range	711
Interquartile range (IQR)	18

Descriptive statistics

Standard deviation	36.402671
Coefficient of variation (CV)	1.9601766
Kurtosis	62.672983
Mean	18.571118
Median Absolute Deviation (MAD)	6
Skewness	6.279766
Sum	256560
Variance	1325.1545
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1645	11.9%
1	1067	7.7%
2	1008	7.3%
3	920	6.7%
4	749	5.4%
5	640	4.6%
6	584	4.2%
8	456	3.3%
7	449	3.3%
9	373	2.7%
Other values (256)	5924	42.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1645	11.9%
1	1067	7.7%
2	1008	7.3%
3	920	6.7%
4	749	5.4%
5	640	4.6%
6	584	4.2%
7	449	3.3%
8	456	3.3%
9	373	2.7%

Value	Count	Frequency (%)
711	1	< 0.1%
602	1	< 0.1%
599	1	< 0.1%
550	1	< 0.1%
536	1	< 0.1%
533	1	< 0.1%
520	1	< 0.1%
494	1	< 0.1%
486	1	< 0.1%
454	1	< 0.1%

last_review
Categorical

HIGH CARDINALITY MISSING

Distinct	1243
Distinct (%)	10.2%
Missing	1645
Missing (%)	11.9%
Memory size	215.9 KiB

2022-09-18	409
2022-09-19	284
2022-09-11	259
2022-08-28	253
2022-09-04	239
Other values (1238)	10726

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	121700
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	526 ?
Unique (%)	4.3%

Sample

1st row	2022-06-21
2nd row	2022-08-09
3rd row	2022-09-10
4th row	2022-08-04
5th row	2022-08-22

Common Values

Value	Count	Frequency (%)
2022-09-18	409	3.0%
2022-09-19	284	2.1%
2022-09-11	259	1.9%
2022-08-28	253	1.8%
2022-09-04	239	1.7%
2022-08-21	220	1.6%
2022-08-07	211	1.5%
2022-08-29	189	1.4%
2022-07-31	185	1.3%
2022-08-14	158	1.1%
Other values (1233)	9763	70.7%
(Missing)	1645	11.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2022-09-18	409	3.4%
2022-09-19	284	2.3%
2022-09-11	259	2.1%
2022-08-28	253	2.1%
2022-09-04	239	2.0%
2022-08-21	220	1.8%
2022-08-07	211	1.7%
2022-08-29	189	1.6%
2022-07-31	185	1.5%
2022-08-14	158	1.3%
Other values (1233)	9763	80.2%

Most occurring characters

Value	Count	Frequency (%)
2	38181	31.4%
0	28843	23.7%
-	24340	20.0%
1	9001	7.4%
8	5727	4.7%
9	5510	4.5%
7	3545	2.9%
3	2006	1.6%
6	1716	1.4%
5	1418	1.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	97360	80.0%
Dash Punctuation	24340	20.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
2	38181	39.2%
0	28843	29.6%
1	9001	9.2%
8	5727	5.9%
9	5510	5.7%
7	3545	3.6%
3	2006	2.1%
6	1716	1.8%
5	1418	1.5%
4	1413	1.5%

Dash Punctuation

Value	Count	Frequency (%)
-	24340	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	121700	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
2	38181	31.4%
0	28843	23.7%
-	24340	20.0%
1	9001	7.4%
8	5727	4.7%
9	5510	4.5%
7	3545	2.9%
3	2006	1.6%
6	1716	1.4%
5	1418	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	121700	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
2	38181	31.4%
0	28843	23.7%
-	24340	20.0%
1	9001	7.4%
8	5727	4.7%
9	5510	4.5%
7	3545	2.9%
3	2006	1.6%
6	1716	1.4%
5	1418	1.2%

reviews_per_month
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	562
Distinct (%)	4.6%
Missing	1645
Missing (%)	11.9%
Infinite	0
Infinite (%)	0.0%
Mean	0.90381594

Minimum	0.01
Maximum	24.02
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0.01
5-th percentile	0.07
Q1	0.23
median	0.53
Q3	1.12
95-th percentile	3
Maximum	24.02
Range	24.01
Interquartile range (IQR)	0.89

Descriptive statistics

Standard deviation	1.1107542
Coefficient of variation (CV)	1.2289607
Kurtosis	33.81649
Mean	0.90381594
Median Absolute Deviation (MAD)	0.37
Skewness	3.8100193
Sum	10999.44
Variance	1.233775
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	225	1.6%
0.08	204	1.5%
0.14	168	1.2%
0.18	160	1.2%
0.27	159	1.2%
0.22	157	1.1%
0.15	157	1.1%
0.13	154	1.1%
0.21	153	1.1%
0.19	151	1.1%
Other values (552)	10482	75.9%
(Missing)	1645	11.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.01	42	0.3%
0.02	71	0.5%
0.03	132	1.0%
0.04	98	0.7%
0.05	122	0.9%
0.06	118	0.9%
0.07	139	1.0%
0.08	204	1.5%
0.09	120	0.9%
0.1	147	1.1%

Value	Count	Frequency (%)
24.02	1	< 0.1%
19.23	1	< 0.1%
15.74	1	< 0.1%
11.34	1	< 0.1%
10.9	1	< 0.1%
10.68	1	< 0.1%
10.48	1	< 0.1%
10.03	1	< 0.1%
9.99	1	< 0.1%
9.95	1	< 0.1%

calculated_host_listings_count
Real number (ℝ)

Distinct	20
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.4006515

Minimum	1
Maximum	179
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	1
Q3	1
95-th percentile	4
Maximum	179
Range	178
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	21.298565
Coefficient of variation (CV)	4.8398664
Kurtosis	56.722032
Mean	4.4006515
Median Absolute Deviation (MAD)	0
Skewness	7.4805991
Sum	60795
Variance	453.62888
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
1	11763	85.1%
2	1068	7.7%
3	234	1.7%
179	179	1.3%
82	82	0.6%
4	68	0.5%
55	55	0.4%
5	50	0.4%
7	49	0.4%
6	48	0.3%
Other values (10)	219	1.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	11763	85.1%
2	1068	7.7%
3	234	1.7%
4	68	0.5%
5	50	0.4%
6	48	0.3%
7	49	0.4%
8	16	0.1%
9	9	0.1%
10	40	0.3%

Value	Count	Frequency (%)
179	179	1.3%
82	82	0.6%
55	55	0.4%
26	26	0.2%
24	24	0.2%
17	17	0.1%
16	16	0.1%
13	26	0.2%
12	12	0.1%
11	33	0.2%

availability_365
Real number (ℝ)

Distinct	366
Distinct (%)	2.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	93.302642

Minimum	0
Maximum	365
Zeros	4846
Zeros (%)	35.1%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	20
Q3	174
95-th percentile	353
Maximum	365
Range	365
Interquartile range (IQR)	174

Descriptive statistics

Standard deviation	123.16196
Coefficient of variation (CV)	1.3200265
Kurtosis	-0.41460319
Mean	93.302642
Median Absolute Deviation (MAD)	20
Skewness	1.060592
Sum	1288976
Variance	15168.868
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	4846	35.1%
1	265	1.9%
365	180	1.3%
2	178	1.3%
8	160	1.2%
4	159	1.2%
3	158	1.1%
266	151	1.1%
7	150	1.1%
6	136	1.0%
Other values (356)	7432	53.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	4846	35.1%
1	265	1.9%
2	178	1.3%
3	158	1.1%
4	159	1.2%
5	130	0.9%
6	136	1.0%
7	150	1.1%
8	160	1.2%
9	131	0.9%

Value	Count	Frequency (%)
365	180	1.3%
364	90	0.7%
363	53	0.4%
362	53	0.4%
361	20	0.1%
360	29	0.2%
359	45	0.3%
358	96	0.7%
357	37	0.3%
356	31	0.2%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	101
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.4511039

Minimum	0
Maximum	471
Zeros	3898
Zeros (%)	28.2%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	3
Q3	6
95-th percentile	19
Maximum	471
Range	471
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	11.373977
Coefficient of variation (CV)	2.0865457
Kurtosis	385.20437
Mean	5.4511039
Median Absolute Deviation (MAD)	3
Skewness	13.410917
Sum	75307
Variance	129.36736
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	3898	28.2%
1	1523	11.0%
2	1395	10.1%
3	1220	8.8%
4	969	7.0%
5	768	5.6%
6	672	4.9%
7	492	3.6%
8	394	2.9%
9	331	2.4%
Other values (91)	2153	15.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	3898	28.2%
1	1523	11.0%
2	1395	10.1%
3	1220	8.8%
4	969	7.0%
5	768	5.6%
6	672	4.9%
7	492	3.6%
8	394	2.9%
9	331	2.4%

Value	Count	Frequency (%)
471	1	< 0.1%
411	1	< 0.1%
275	1	< 0.1%
216	1	< 0.1%
198	1	< 0.1%
197	1	< 0.1%
182	1	< 0.1%
154	1	< 0.1%
150	1	< 0.1%
144	2	< 0.1%

license
Unsupported

MISSING REJECTED UNSUPPORTED

Missing	13815
Missing (%)	100.0%
Memory size	215.9 KiB

property_type
Categorical

HIGH CARDINALITY HIGH CORRELATION IMBALANCE

Distinct	52
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	215.9 KiB

Entire rental unit	6067
Entire condo	4329
Private room in rental unit	840
Entire home	656
Private room in condo	348
Other values (47)	1575

Length

Max length	34
Median length	33
Mean length	16.45067
Min length	3

Characters and Unicode

Total characters	227266
Distinct characters	35
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10 ?
Unique (%)	0.1%

Sample

1st row	Entire rental unit
2nd row	Entire home
3rd row	Entire townhouse
4th row	Entire rental unit
5th row	Entire condo

Common Values

Value	Count	Frequency (%)
Entire rental unit	6067	43.9%
Entire condo	4329	31.3%
Private room in rental unit	840	6.1%
Entire home	656	4.7%
Private room in condo	348	2.5%
Entire townhouse	346	2.5%
Entire serviced apartment	344	2.5%
Entire villa	230	1.7%
Private room in home	138	1.0%
Entire loft	135	1.0%
Other values (42)	382	2.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
entire	12187	32.0%
unit	6913	18.1%
rental	6913	18.1%
condo	4679	12.3%
room	1586	4.2%
in	1584	4.2%
private	1519	4.0%
home	819	2.1%
townhouse	384	1.0%
apartment	358	0.9%
Other values (28)	1161	3.0%

Most occurring characters

Value	Count	Frequency (%)
n	33089	14.6%
t	29047	12.8%
	24288	10.7%
e	23210	10.2%
r	22952	10.1%
i	22926	10.1%
o	14442	6.4%
E	12187	5.4%
a	9654	4.2%
l	7702	3.4%
Other values (25)	27769	12.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	189148	83.2%
Space Separator	24288	10.7%
Uppercase Letter	13825	6.1%
Other Punctuation	5	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	33089	17.5%
t	29047	15.4%
e	23210	12.3%
r	22952	12.1%
i	22926	12.1%
o	14442	7.6%
a	9654	5.1%
l	7702	4.1%
u	7521	4.0%
d	5098	2.7%
Other values (13)	13507	7.1%

Uppercase Letter

Value	Count	Frequency (%)
E	12187	88.2%
P	1519	11.0%
R	53	0.4%
S	19	0.1%
H	14	0.1%
T	13	0.1%
B	9	0.1%
C	5	< 0.1%
V	5	< 0.1%
M	1	< 0.1%

Space Separator

Value	Count	Frequency (%)
	24288	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	5	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	202973	89.3%
Common	24293	10.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	33089	16.3%
t	29047	14.3%
e	23210	11.4%
r	22952	11.3%
i	22926	11.3%
o	14442	7.1%
E	12187	6.0%
a	9654	4.8%
l	7702	3.8%
u	7521	3.7%
Other values (23)	20243	10.0%

Common

Value	Count	Frequency (%)
	24288	> 99.9%
/	5	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	227266	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	33089	14.6%
t	29047	12.8%
	24288	10.7%
e	23210	10.2%
r	22952	10.1%
i	22926	10.1%
o	14442	6.4%
E	12187	5.4%
a	9654	4.2%
l	7702	3.4%
Other values (25)	27769	12.2%

accommodates
Real number (ℝ)

Distinct	16
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.4003619

Minimum	0
Maximum	16
Zeros	2
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	2
median	3
Q3	4
95-th percentile	6
Maximum	16
Range	16
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.7283396
Coefficient of variation (CV)	0.50828108
Kurtosis	2.7733747
Mean	3.4003619
Median Absolute Deviation (MAD)	1
Skewness	1.3250253
Sum	46976
Variance	2.9871578
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=16)

Value	Count	Frequency (%)
2	5559	40.2%
4	3240	23.5%
3	1677	12.1%
6	1180	8.5%
5	1072	7.8%
1	487	3.5%
8	268	1.9%
7	184	1.3%
10	70	0.5%
9	39	0.3%
Other values (6)	39	0.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	2	< 0.1%
1	487	3.5%
2	5559	40.2%
3	1677	12.1%
4	3240	23.5%
5	1072	7.8%
6	1180	8.5%
7	184	1.3%
8	268	1.9%
9	39	0.3%

Value	Count	Frequency (%)
16	4	< 0.1%
15	2	< 0.1%
14	1	< 0.1%
12	19	0.1%
11	11	0.1%
10	70	0.5%
9	39	0.3%
8	268	1.9%
7	184	1.3%
6	1180	8.5%

first_review
Categorical

HIGH CARDINALITY MISSING

Distinct	2708
Distinct (%)	22.3%
Missing	1645
Missing (%)	11.9%
Memory size	215.9 KiB

2022-08-07	63
2022-07-03	62
2022-05-29	58
2022-08-06	54
2022-04-17	51
Other values (2703)	11882

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	121700
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	844 ?
Unique (%)	6.9%

Sample

1st row	2009-09-04
2nd row	2013-12-02
3rd row	2010-10-14
4th row	2010-06-17
5th row	2010-08-16

Common Values

Value	Count	Frequency (%)
2022-08-07	63	0.5%
2022-07-03	62	0.4%
2022-05-29	58	0.4%
2022-08-06	54	0.4%
2022-04-17	51	0.4%
2022-07-24	50	0.4%
2022-06-06	50	0.4%
2022-06-19	48	0.3%
2022-04-18	47	0.3%
2022-08-08	47	0.3%
Other values (2698)	11640	84.3%
(Missing)	1645	11.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2022-08-07	63	0.5%
2022-07-03	62	0.5%
2022-05-29	58	0.5%
2022-08-06	54	0.4%
2022-04-17	51	0.4%
2022-07-24	50	0.4%
2022-06-06	50	0.4%
2022-06-19	48	0.4%
2022-04-18	47	0.4%
2022-08-08	47	0.4%
Other values (2698)	11640	95.6%

Most occurring characters

Value	Count	Frequency (%)
0	29007	23.8%
2	27784	22.8%
-	24340	20.0%
1	15900	13.1%
7	4868	4.0%
8	4642	3.8%
9	3654	3.0%
6	3500	2.9%
5	2938	2.4%
3	2573	2.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	97360	80.0%
Dash Punctuation	24340	20.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	29007	29.8%
2	27784	28.5%
1	15900	16.3%
7	4868	5.0%
8	4642	4.8%
9	3654	3.8%
6	3500	3.6%
5	2938	3.0%
3	2573	2.6%
4	2494	2.6%

Dash Punctuation

Value	Count	Frequency (%)
-	24340	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	121700	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	29007	23.8%
2	27784	22.8%
-	24340	20.0%
1	15900	13.1%
7	4868	4.0%
8	4642	3.8%
9	3654	3.0%
6	3500	2.9%
5	2938	2.4%
3	2573	2.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	121700	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	29007	23.8%
2	27784	22.8%
-	24340	20.0%
1	15900	13.1%
7	4868	4.0%
8	4642	3.8%
9	3654	3.0%
6	3500	2.9%
5	2938	2.4%
3	2573	2.1%

review_scores_value
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	128
Distinct (%)	1.1%
Missing	1688
Missing (%)	12.2%
Infinite	0
Infinite (%)	0.0%
Mean	4.7203134

Minimum	1
Maximum	5
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	1
5-th percentile	4.14
Q1	4.61
median	4.78
Q3	5
95-th percentile	5
Maximum	5
Range	4
Interquartile range (IQR)	0.39

Descriptive statistics

Standard deviation	0.30828518
Coefficient of variation (CV)	0.06531032
Kurtosis	18.598551
Mean	4.7203134
Median Absolute Deviation (MAD)	0.18
Skewness	-2.934072
Sum	57243.24
Variance	0.09503975
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	3037	22.0%
4.67	639	4.6%
4.5	622	4.5%
4.75	507	3.7%
4.8	397	2.9%
4	377	2.7%
4.83	333	2.4%
4.86	271	2.0%
4.88	258	1.9%
4.71	236	1.7%
Other values (118)	5450	39.4%
(Missing)	1688	12.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	6	< 0.1%
2	5	< 0.1%
2.33	1	< 0.1%
2.4	1	< 0.1%
2.5	3	< 0.1%
2.67	1	< 0.1%
3	49	0.4%
3.18	1	< 0.1%
3.2	2	< 0.1%
3.23	1	< 0.1%

Value	Count	Frequency (%)
5	3037	22.0%
4.98	2	< 0.1%
4.97	13	0.1%
4.96	24	0.2%
4.95	43	0.3%
4.94	69	0.5%
4.93	100	0.7%
4.92	143	1.0%
4.91	100	0.7%
4.9	133	1.0%

review_scores_cleanliness
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	171
Distinct (%)	1.4%
Missing	1688
Missing (%)	12.2%
Infinite	0
Infinite (%)	0.0%
Mean	4.692313

Minimum	0
Maximum	5
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	4
Q1	4.56
median	4.8
Q3	5
95-th percentile	5
Maximum	5
Range	5
Interquartile range (IQR)	0.44

Descriptive statistics

Standard deviation	0.3942319
Coefficient of variation (CV)	0.084016538
Kurtosis	14.23205
Mean	4.692313
Median Absolute Deviation (MAD)	0.2
Skewness	-2.7881466
Sum	56903.68
Variance	0.15541879
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	3479	25.2%
4.5	546	4.0%
4.67	476	3.4%
4	447	3.2%
4.75	388	2.8%
4.8	286	2.1%
4.83	283	2.0%
4.88	248	1.8%
4.86	216	1.6%
4.33	210	1.5%
Other values (161)	5548	40.2%
(Missing)	1688	12.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	11	0.1%
1.67	1	< 0.1%
1.88	1	< 0.1%
2	13	0.1%
2.33	2	< 0.1%
2.4	1	< 0.1%
2.5	4	< 0.1%
2.6	1	< 0.1%
2.67	3	< 0.1%

Value	Count	Frequency (%)
5	3479	25.2%
4.99	14	0.1%
4.98	51	0.4%
4.97	75	0.5%
4.96	91	0.7%
4.95	111	0.8%
4.94	154	1.1%
4.93	128	0.9%
4.92	174	1.3%
4.91	133	1.0%

review_scores_location
Real number (ℝ)

Distinct	108
Distinct (%)	0.9%
Missing	1689
Missing (%)	12.2%
Infinite	0
Infinite (%)	0.0%
Mean	4.8263986

Minimum	1
Maximum	5
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	1
5-th percentile	4.4
Q1	4.75
median	4.9
Q3	5
95-th percentile	5
Maximum	5
Range	4
Interquartile range (IQR)	0.25

Descriptive statistics

Standard deviation	0.25385234
Coefficient of variation (CV)	0.052596637
Kurtosis	29.868369
Mean	4.8263986
Median Absolute Deviation (MAD)	0.1
Skewness	-3.7504684
Sum	58524.91
Variance	0.064441009
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	4740	34.3%
4.67	408	3.0%
4.5	402	2.9%
4.75	379	2.7%
4.8	329	2.4%
4.83	323	2.3%
4.88	322	2.3%
4.89	276	2.0%
4.86	267	1.9%
4.92	229	1.7%
Other values (98)	4451	32.2%
(Missing)	1689	12.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	4	< 0.1%
2	3	< 0.1%
2.5	1	< 0.1%
2.6	1	< 0.1%
2.86	1	< 0.1%
3	26	0.2%
3.17	1	< 0.1%
3.25	2	< 0.1%
3.33	1	< 0.1%
3.5	12	0.1%

Value	Count	Frequency (%)
5	4740	34.3%
4.99	23	0.2%
4.98	78	0.6%
4.97	123	0.9%
4.96	122	0.9%
4.95	148	1.1%
4.94	204	1.5%
4.93	182	1.3%
4.92	229	1.7%
4.91	181	1.3%

review_scores_accuracy
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	118
Distinct (%)	1.0%
Missing	1688
Missing (%)	12.2%
Infinite	0
Infinite (%)	0.0%
Mean	4.8453377

Minimum	0
Maximum	5
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	4.43
Q1	4.8
median	4.93
Q3	5
95-th percentile	5
Maximum	5
Range	5
Interquartile range (IQR)	0.2

Descriptive statistics

Standard deviation	0.26261914
Coefficient of variation (CV)	0.054200378
Kurtosis	49.131053
Mean	4.8453377
Median Absolute Deviation (MAD)	0.07
Skewness	-5.0182151
Sum	58759.41
Variance	0.06896881
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	5073	36.7%
4.88	356	2.6%
4.67	351	2.5%
4.75	323	2.3%
4.86	321	2.3%
4.5	319	2.3%
4.83	315	2.3%
4.8	302	2.2%
4.92	273	2.0%
4.9	246	1.8%
Other values (108)	4248	30.7%
(Missing)	1688	12.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	7	0.1%
2	3	< 0.1%
2.5	4	< 0.1%
3	24	0.2%
3.25	2	< 0.1%
3.33	6	< 0.1%
3.38	1	< 0.1%
3.4	2	< 0.1%
3.41	1	< 0.1%

Value	Count	Frequency (%)
5	5073	36.7%
4.99	16	0.1%
4.98	69	0.5%
4.97	126	0.9%
4.96	169	1.2%
4.95	214	1.5%
4.94	242	1.8%
4.93	243	1.8%
4.92	273	2.0%
4.91	241	1.7%

review_scores_communication
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	117
Distinct (%)	1.0%
Missing	1688
Missing (%)	12.2%
Infinite	0
Infinite (%)	0.0%
Mean	4.9051398

Minimum	1
Maximum	5
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	1
5-th percentile	4.55
Q1	4.9
median	5
Q3	5
95-th percentile	5
Maximum	5
Range	4
Interquartile range (IQR)	0.1

Descriptive statistics

Standard deviation	0.2354877
Coefficient of variation (CV)	0.048008357
Kurtosis	81.809339
Mean	4.9051398
Median Absolute Deviation (MAD)	0
Skewness	-7.0826237
Sum	59484.63
Variance	0.055454456
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	6926	50.1%
4.94	311	2.3%
4.88	273	2.0%
4.93	270	2.0%
4.96	263	1.9%
4.92	253	1.8%
4.97	245	1.8%
4.95	238	1.7%
4.89	229	1.7%
4.9	214	1.5%
Other values (107)	2905	21.0%
(Missing)	1688	12.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	9	0.1%
2	6	< 0.1%
2.25	1	< 0.1%
2.33	1	< 0.1%
2.5	1	< 0.1%
3	18	0.1%
3.17	1	< 0.1%
3.25	3	< 0.1%
3.33	2	< 0.1%
3.38	1	< 0.1%

Value	Count	Frequency (%)
5	6926	50.1%
4.99	54	0.4%
4.98	172	1.2%
4.97	245	1.8%
4.96	263	1.9%
4.95	238	1.7%
4.94	311	2.3%
4.93	270	2.0%
4.92	253	1.8%
4.91	184	1.3%

review_scores_checkin
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	107
Distinct (%)	0.9%
Missing	1688
Missing (%)	12.2%
Infinite	0
Infinite (%)	0.0%
Mean	4.88394

Minimum	0
Maximum	5
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	4.5
Q1	4.86
median	4.98
Q3	5
95-th percentile	5
Maximum	5
Range	5
Interquartile range (IQR)	0.14

Descriptive statistics

Standard deviation	0.25234041
Coefficient of variation (CV)	0.051667386
Kurtosis	82.268207
Mean	4.88394
Median Absolute Deviation (MAD)	0.02
Skewness	-7.0078609
Sum	59227.54
Variance	0.063675684
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	6023	43.6%
4.88	341	2.5%
4.94	303	2.2%
4.92	288	2.1%
4.86	271	2.0%
4.93	266	1.9%
4.9	260	1.9%
4.95	255	1.8%
4.75	247	1.8%
4.91	246	1.8%
Other values (97)	3627	26.3%
(Missing)	1688	12.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	11	0.1%
2	8	0.1%
2.67	3	< 0.1%
2.75	1	< 0.1%
3	18	0.1%
3.17	1	< 0.1%
3.33	1	< 0.1%
3.38	1	< 0.1%
3.4	1	< 0.1%

Value	Count	Frequency (%)
5	6023	43.6%
4.99	27	0.2%
4.98	145	1.0%
4.97	199	1.4%
4.96	229	1.7%
4.95	255	1.8%
4.94	303	2.2%
4.93	266	1.9%
4.92	288	2.1%
4.91	246	1.8%

review_scores_rating
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	124
Distinct (%)	1.0%
Missing	1645
Missing (%)	11.9%
Infinite	0
Infinite (%)	0.0%
Mean	4.7871446

Minimum	0
Maximum	5
Zeros	43
Zeros (%)	0.3%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	0
5-th percentile	4.29
Q1	4.71
median	4.89
Q3	5
95-th percentile	5
Maximum	5
Range	5
Interquartile range (IQR)	0.29

Descriptive statistics

Standard deviation	0.40075638
Coefficient of variation (CV)	0.083715119
Kurtosis	75.709681
Mean	4.7871446
Median Absolute Deviation (MAD)	0.11
Skewness	-7.2082105
Sum	58259.55
Variance	0.16060568
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	4483	32.5%
4.67	460	3.3%
4.5	377	2.7%
4.75	353	2.6%
4.83	333	2.4%
4.8	327	2.4%
4.88	305	2.2%
4.86	291	2.1%
4	240	1.7%
4.92	229	1.7%
Other values (114)	4772	34.5%
(Missing)	1645	11.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	43	0.3%
1	5	< 0.1%
2	5	< 0.1%
2.5	2	< 0.1%
2.75	1	< 0.1%
2.8	1	< 0.1%
3	36	0.3%
3.2	1	< 0.1%
3.25	1	< 0.1%
3.29	1	< 0.1%

Value	Count	Frequency (%)
5	4483	32.5%
4.99	7	0.1%
4.98	39	0.3%
4.97	86	0.6%
4.96	121	0.9%
4.95	130	0.9%
4.94	212	1.5%
4.93	179	1.3%
4.92	229	1.7%
4.91	212	1.5%

maximum_nights
Real number (ℝ)

Distinct	144
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	472.61158

Minimum	1
Maximum	4000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	215.9 KiB

Quantile statistics

Minimum	1
5-th percentile	7
Q1	18
median	365
Q3	1125
95-th percentile	1125
Maximum	4000
Range	3999
Interquartile range (IQR)	1107

Descriptive statistics

Standard deviation	499.80258
Coefficient of variation (CV)	1.0575335
Kurtosis	-1.3055101
Mean	472.61158
Median Absolute Deviation (MAD)	352
Skewness	0.48923997
Sum	6529129
Variance	249802.62
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1125	4792	34.7%
365	2237	16.2%
14	1123	8.1%
30	837	6.1%
7	716	5.2%
21	458	3.3%
10	422	3.1%
20	266	1.9%
60	233	1.7%
28	182	1.3%
Other values (134)	2549	18.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	10	0.1%
2	29	0.2%
3	77	0.6%
4	109	0.8%
5	141	1.0%
6	143	1.0%
7	716	5.2%
8	154	1.1%
9	70	0.5%
10	422	3.1%

Value	Count	Frequency (%)
4000	2	< 0.1%
1125	4792	34.7%
1124	50	0.4%
1123	1	< 0.1%
1122	2	< 0.1%
1120	14	0.1%
1111	1	< 0.1%
1100	2	< 0.1%
1095	1	< 0.1%
1000	13	0.1%

listing_url
Categorical

HIGH CARDINALITY UNIFORM UNIQUE

Distinct	13815
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	215.9 KiB

https://www.airbnb.com/rooms/6983	1
https://www.airbnb.com/rooms/52756315	1
https://www.airbnb.com/rooms/52769733	1
https://www.airbnb.com/rooms/52782453	1
https://www.airbnb.com/rooms/52782721	1
Other values (13810)	13810

Length

Max length	47
Median length	37
Mean length	39.507275
Min length	33

Characters and Unicode

Total characters	545793
Distinct characters	26
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	13815 ?
Unique (%)	100.0%

Sample

1st row	https://www.airbnb.com/rooms/6983
2nd row	https://www.airbnb.com/rooms/26057
3rd row	https://www.airbnb.com/rooms/26473
4th row	https://www.airbnb.com/rooms/29118
5th row	https://www.airbnb.com/rooms/31094

Common Values

Value	Count	Frequency (%)
https://www.airbnb.com/rooms/6983	1	< 0.1%
https://www.airbnb.com/rooms/52756315	1	< 0.1%
https://www.airbnb.com/rooms/52769733	1	< 0.1%
https://www.airbnb.com/rooms/52782453	1	< 0.1%
https://www.airbnb.com/rooms/52782721	1	< 0.1%
https://www.airbnb.com/rooms/52786282	1	< 0.1%
https://www.airbnb.com/rooms/52795544	1	< 0.1%
https://www.airbnb.com/rooms/52800828	1	< 0.1%
https://www.airbnb.com/rooms/52803374	1	< 0.1%
https://www.airbnb.com/rooms/52804784	1	< 0.1%
Other values (13805)	13805	99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
https://www.airbnb.com/rooms/6983	1	< 0.1%
https://www.airbnb.com/rooms/313237	1	< 0.1%
https://www.airbnb.com/rooms/26473	1	< 0.1%
https://www.airbnb.com/rooms/29118	1	< 0.1%
https://www.airbnb.com/rooms/31094	1	< 0.1%
https://www.airbnb.com/rooms/32379	1	< 0.1%
https://www.airbnb.com/rooms/32841	1	< 0.1%
https://www.airbnb.com/rooms/33680	1	< 0.1%
https://www.airbnb.com/rooms/37159	1	< 0.1%
https://www.airbnb.com/rooms/38499	1	< 0.1%
Other values (13805)	13805	99.9%

Most occurring characters

Value	Count	Frequency (%)
/	55260	10.1%
w	41445	7.6%
o	41445	7.6%
s	27630	5.1%
.	27630	5.1%
r	27630	5.1%
b	27630	5.1%
t	27630	5.1%
m	27630	5.1%
6	15993	2.9%
Other values (16)	225870	41.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	303930	55.7%
Decimal Number	145158	26.6%
Other Punctuation	96705	17.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
w	41445	13.6%
o	41445	13.6%
s	27630	9.1%
r	27630	9.1%
b	27630	9.1%
t	27630	9.1%
m	27630	9.1%
h	13815	4.5%
c	13815	4.5%
n	13815	4.5%
Other values (3)	41445	13.6%

Decimal Number

Value	Count	Frequency (%)
6	15993	11.0%
5	15549	10.7%
3	15459	10.6%
1	15146	10.4%
4	15051	10.4%
2	14913	10.3%
7	13579	9.4%
0	13228	9.1%
9	13175	9.1%
8	13065	9.0%

Other Punctuation

Value	Count	Frequency (%)
/	55260	57.1%
.	27630	28.6%
:	13815	14.3%

Most occurring scripts

Value	Count	Frequency (%)
Latin	303930	55.7%
Common	241863	44.3%

Most frequent character per script

Common

Value	Count	Frequency (%)
/	55260	22.8%
.	27630	11.4%
6	15993	6.6%
5	15549	6.4%
3	15459	6.4%
1	15146	6.3%
4	15051	6.2%
2	14913	6.2%
:	13815	5.7%
7	13579	5.6%
Other values (3)	39468	16.3%

Latin

Value	Count	Frequency (%)
w	41445	13.6%
o	41445	13.6%
s	27630	9.1%
r	27630	9.1%
b	27630	9.1%
t	27630	9.1%
m	27630	9.1%
h	13815	4.5%
c	13815	4.5%
n	13815	4.5%
Other values (3)	41445	13.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	545793	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
/	55260	10.1%
w	41445	7.6%
o	41445	7.6%
s	27630	5.1%
.	27630	5.1%
r	27630	5.1%
b	27630	5.1%
t	27630	5.1%
m	27630	5.1%
6	15993	2.9%
Other values (16)	225870	41.4%

host_is_superhost
Boolean

Distinct	2
Distinct (%)	< 0.1%
Missing	2
Missing (%)	< 0.1%
Memory size	134.9 KiB

False	12310
True	1503
(Missing)	2

Common Values (Table)
Common Values (Plot)

Value	Count	Frequency (%)
False	12310	89.1%
True	1503	10.9%
(Missing)	2	< 0.1%

host_about
Categorical

HIGH CARDINALITY MISSING

Distinct	5856
Distinct (%)	85.6%
Missing	6970
Missing (%)	50.5%
Memory size	215.9 KiB

Vi udlejer møblerede lejligheder og har mere end 10 års erfaring. Vores mission er at udleje fuldt møblerede lejligheder og hotellejligheder med de bedste beliggenheder i København til turister, forretningsrejsende, virksomheder og deres medarbejdere. Vi er til stede i København og står til rådighed 24/7 for vore gæster og ejere.	179
Sanders provides quality stays around the world with a hotel-like service done better.	55
Seneca Service Company has a variety of furnished services apartments located in Central Copenhagen.	26
Rent A Place, has apartments on the best locations primary in Inner City of Copenhagen. We are only located in the center of the center and sights, shopping or metro is just a small walk away. . Are you looking for prime locations, cosy interiors and attentive service? We’re here to give you a welcoming & local experience in Copenhagen. All of our apartments has fast wifi and complimentary in-room service. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way, is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the privacy and flexibility during your visit. Check in to your many of our apartments at your own with your own passcode. ‍ If you need us during your stay, are we always a sms away. Rent A Place	24
.	23
Other values (5851)	6538

Length

Max length	6640
Median length	788
Mean length	261.37327
Min length	1

Characters and Unicode

Total characters	1789100
Distinct characters	243
Distinct categories	20 ?
Distinct scripts	7 ?
Distinct blocks	9 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5433 ?
Unique (%)	79.4%

Sample

1st row	I'm currently working as an environmental consultant for a large engineering consultancy in Copenhagen. When I'm not at work, I spend time doing sports (playing football, running, cross fit), or doing indoor activities such as reading books and listening to music. I have recently taken an interest in cooking, and I love great food. I'm outgoing, happy and love good company. And I love my bike as any other person from Copenhagen..
2nd row	We are a family with 2 children, and living in a great and beautiful house placed in a very special part in central Copenhagen called Kartoffelraekkerne. It's like a small village in the center of the city! The house is available from time to time - when we are travelling/working abroad. We're looking forward to hearing from you.
3rd row	Active young woman, started as an expat in Copenhagen and settled it as a new home. During this process, I have learnt a lot about the art of housing, and made it my daily activity. While I prefer to let you conquer the city on your own paths, please feel free to use my experience if you need any tips & advice. Will do my best to give you a perfect slice of Copenhagen. In my spare time I love consuming good food, taking long bike rides or discovering new music. I live & work here and can be contacted with any comments & questions.
4th row	I have a Master of Arts in Musicology and I work as a vocal coach and a singer and I LOVE to travel and meet people from all over the world. I live in lively Vesterbro with my 5 yr. old son Wili.
5th row	Hi and welcome. My name is Ebbe, I am a medical doctor working in Copenhagen. I live in the flat with my girlfriend Lea who is working as a nurse. We have two little girls: Nora is 6 years old and Luna is 2 years old. We love sports, music and travelling, and we look forward welcoming you to Wonderful Copenhagen :-)

Common Values

Value	Count	Frequency (%)
Vi udlejer møblerede lejligheder og har mere end 10 års erfaring. Vores mission er at udleje fuldt møblerede lejligheder og hotellejligheder med de bedste beliggenheder i København til turister, forretningsrejsende, virksomheder og deres medarbejdere. Vi er til stede i København og står til rådighed 24/7 for vore gæster og ejere.	179	1.3%
Sanders provides quality stays around the world with a hotel-like service done better.	55	0.4%
Seneca Service Company has a variety of furnished services apartments located in Central Copenhagen.	26	0.2%
Rent A Place, has apartments on the best locations primary in Inner City of Copenhagen. We are only located in the center of the center and sights, shopping or metro is just a small walk away. . Are you looking for prime locations, cosy interiors and attentive service? We’re here to give you a welcoming & local experience in Copenhagen. All of our apartments has fast wifi and complimentary in-room service. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way, is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the privacy and flexibility during your visit. Check in to your many of our apartments at your own with your own passcode. ‍ If you need us during your stay, are we always a sms away. Rent A Place	24	0.2%
.	23	0.2%
We’re Blueground, a global proptech company with several thousand move-in-ready apartments in a growing number of major cities around the world. With flexible terms and homes in vibrant, centrally based neighborhoods, you’ll feel at home and free to roam for as long as you want — a month, a year, or longer. Each apartment is thoughtfully designed with exclusive furnishings, fully equipped kitchens, and incredible amenities – making every day a five-star experience. From day one, you’ll enjoy high-speed Wi-Fi, premium linens, and smart home entertainment. Plus, access to pools, gyms, and outdoor spaces in select buildings. Why stress over your apartment? We provide a hassle-free alternative — a consistent, quality guest experience that starts even before you arrive. Because we let you book our most up-to-date apartment listings online, confirm with a click, pay securely, and check in easily. During your stay Upon arrival, you’ll either be greeted personally by a Blueground team member or given self-check-in instructions. The entire apartment is yours! You’ll enjoy reliable support via email, phone, and our Guest App, where you can request everything from a home cleaning to extra towels. We’ll share all details upon confirmation of your stay.	16	0.1%
Are you looking for prime locations, Nordic interiors and attentive service? We’re here to give you a welcoming & truly local experience in Copenhagen. All of our apartments have designer bathrooms, fast wifi and complimentary in-room media entertainment. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the luxury of privacy and flexibility during your visit. Check in to your apartment at your own leisure, day and night, with your passcode. Enjoy a cup of coffee while you hop on the wifi and settle into your new place. Should you have any special requests like a scheduled taxi pick up or laundry service, just let us know. We'll be happy to arrange it for you. ‍ If you need us during your stay, our property managers are always just a call, sms or an email away.	13	0.1%
Located in the best neighbourhoods, Into This Place's unique and high end rentals offer the space and amenities of a hotel apartment combined with the quality and consistency of a boutique hotel. In all our living spaces our finest mission is to make you feel at home and local while away. The design of each living space is unique and our focus is on Scandinavian design combined with lots of danish “hygge” (coziness). By removing some of the services a traditional hotel offers as the 24/7 reception, lobby area, breakfast restaurant etc. and instead having our team anticipate needs and create a high level of behind-the-scenes-service we are able to provide quality living spaces with invisible service at affordable prices. All our listings are licensed and we live up to the high requirements for hotels within fire safety which most other AIRBNB listings unfortunately do not.	13	0.1%
Are you looking for prime locations, Nordic interiors and attentive service? We’re here to give you a welcoming & truly local experience in Copenhagen. All of our apartments have designer bathrooms, fast wifi and complimentary in-room media entertainment. So if you are looking for a comfortable place that lets you do more than just sleep, we are here to help. Making you feel at home and otherwise staying out of your way is our top priority. By preparing everything ahead of time, you and your travel companions will enjoy the luxury of privacy and flexibility during your visit. Check in to your apartment at your own leisure, day and night, with your passcode. Enjoy a cup of coffee while you hop on the wifi and settle into your new place. Should you have any special requests like a scheduled taxi pick up or laundry service, just let us know. We'll be happy to arrange it for you. ‍ If you need us during your stay, our property managers are always just a call, sms or an email away.	12	0.1%
...	11	0.1%
Other values (5846)	6473	46.9%
(Missing)	6970	50.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
and	14729	4.6%
i	10684	3.3%
in	8093	2.5%
a	7976	2.5%
to	7885	2.5%
the	7354	2.3%
my	4943	1.5%
of	4411	1.4%
we	4324	1.3%
copenhagen	3906	1.2%
Other values (14278)	246780	76.9%

Most occurring characters

Value	Count	Frequency (%)
	314251	17.6%
e	176561	9.9%
a	111866	6.3%
n	106796	6.0%
o	103812	5.8%
t	94436	5.3%
i	94267	5.3%
r	92469	5.2%
s	68794	3.8%
l	65472	3.7%
Other values (233)	560376	31.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1343117	75.1%
Space Separator	314267	17.6%
Uppercase Letter	51635	2.9%
Other Punctuation	43761	2.4%
Control	17414	1.0%
Decimal Number	10903	0.6%
Dash Punctuation	3313	0.2%
Close Punctuation	2133	0.1%
Open Punctuation	1120	0.1%
Final Punctuation	901	0.1%
Other values (10)	536	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
い	7	4.4%
の	6	3.8%
に	6	3.8%
し	6	3.8%
ん	5	3.1%
ﾝ	4	2.5%
で	3	1.9%
日	3	1.9%
は	3	1.9%
本	3	1.9%
Other values (80)	113	71.1%

Lowercase Letter

Value	Count	Frequency (%)
e	176561	13.1%
a	111866	8.3%
n	106796	8.0%
o	103812	7.7%
t	94436	7.0%
i	94267	7.0%
r	92469	6.9%
s	68794	5.1%
l	65472	4.9%
d	53267	4.0%
Other values (39)	375377	27.9%

Uppercase Letter

Value	Count	Frequency (%)
I	12209	23.6%
C	5485	10.6%
W	3820	7.4%
A	3394	6.6%
M	2544	4.9%
S	2389	4.6%
D	2331	4.5%
H	2259	4.4%
V	1767	3.4%
T	1670	3.2%
Other values (22)	13767	26.7%

Other Punctuation

Value	Count	Frequency (%)
.	20324	46.4%
,	16102	36.8%
'	2489	5.7%
!	1578	3.6%
:	1449	3.3%
/	697	1.6%
&	372	0.9%
"	317	0.7%
;	203	0.5%
?	151	0.3%
Other values (7)	79	0.2%

Decimal Number

Value	Count	Frequency (%)
2	2099	19.3%
1	1962	18.0%
0	1604	14.7%
3	1309	12.0%
4	1059	9.7%
5	925	8.5%
7	585	5.4%
9	509	4.7%
6	431	4.0%
8	420	3.9%

Control

Value	Count	Frequency (%)
	12120	69.6%
	5272	30.3%
	8	< 0.1%
	6	< 0.1%
	3	< 0.1%
	3	< 0.1%
	2	< 0.1%

Math Symbol

Value	Count	Frequency (%)
+	50	61.0%
=	12	14.6%
<	10	12.2%
\|	5	6.1%
~	4	4.9%
×	1	1.2%

Other Symbol

Value	Count	Frequency (%)
✨	6	31.6%
☺	5	26.3%
❤	4	21.1%
✌	2	10.5%
®	1	5.3%
¦	1	5.3%

Modifier Symbol

Value	Count	Frequency (%)
´	123	92.5%
`	4	3.0%
^	4	3.0%
¨	2	1.5%

Dash Punctuation

Value	Count	Frequency (%)
-	3217	97.1%
–	60	1.8%
—	36	1.1%

Close Punctuation

Value	Count	Frequency (%)
)	2130	99.9%
]	2	0.1%
}	1	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
(	1117	99.7%
[	2	0.2%
{	1	0.1%

Modifier Letter

Value	Count	Frequency (%)
ｰ	3	33.3%
ﾟ	3	33.3%
ﾞ	3	33.3%

Space Separator

Value	Count	Frequency (%)
	314251	> 99.9%
	16	< 0.1%

Final Punctuation

Value	Count	Frequency (%)
’	846	93.9%
”	55	6.1%

Initial Punctuation

Value	Count	Frequency (%)
“	50	87.7%
‘	7	12.3%

Format

Value	Count	Frequency (%)
‍	49	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	15	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	11	100.0%

Other Number

Value	Count	Frequency (%)
½	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1394752	78.0%
Common	394129	22.0%
Hiragana	76	< 0.1%
Inherited	60	< 0.1%
Han	50	< 0.1%
Katakana	18	< 0.1%
Hangul	15	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	176561	12.7%
a	111866	8.0%
n	106796	7.7%
o	103812	7.4%
t	94436	6.8%
i	94267	6.8%
r	92469	6.6%
s	68794	4.9%
l	65472	4.7%
d	53267	3.8%
Other values (71)	427012	30.6%

Common

Value	Count	Frequency (%)
	314251	79.7%
.	20324	5.2%
,	16102	4.1%
	12120	3.1%
	5272	1.3%
-	3217	0.8%
'	2489	0.6%
)	2130	0.5%
2	2099	0.5%
1	1962	0.5%
Other values (60)	14163	3.6%

Han

Value	Count	Frequency (%)
日	3	6.0%
本	3	6.0%
住	3	6.0%
安	2	4.0%
皆	2	4.0%
在	2	4.0%
学	2	4.0%
中	2	4.0%
疲	1	2.0%
旅	1	2.0%
Other values (29)	29	58.0%

Hiragana

Value	Count	Frequency (%)
い	7	9.2%
の	6	7.9%
に	6	7.9%
し	6	7.9%
ん	5	6.6%
で	3	3.9%
は	3	3.9%
ま	3	3.9%
す	3	3.9%
さ	3	3.9%
Other values (20)	31	40.8%

Hangul

Value	Count	Frequency (%)
요	2	13.3%
어	2	13.3%
세	1	6.7%
금	1	6.7%
왔	1	6.7%
배	1	6.7%
조	1	6.7%
가	1	6.7%
극	1	6.7%
한	1	6.7%
Other values (3)	3	20.0%

Katakana

Value	Count	Frequency (%)
ﾝ	4	22.2%
ｹ	3	16.7%
ﾊ	3	16.7%
ｺ	2	11.1%
ﾍ	2	11.1%
ﾄ	2	11.1%
ｱ	1	5.6%
ｽ	1	5.6%

Inherited

Value	Count	Frequency (%)
‍	49	81.7%
️	11	18.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1779975	99.5%
None	7829	0.4%
Punctuation	1127	0.1%
Hiragana	76	< 0.1%
CJK	50	< 0.1%
Hangul	15	< 0.1%
Dingbats	12	< 0.1%
VS	11	< 0.1%
Misc Symbols	5	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	314251	17.7%
e	176561	9.9%
a	111866	6.3%
n	106796	6.0%
o	103812	5.8%
t	94436	5.3%
i	94267	5.3%
r	92469	5.2%
s	68794	3.9%
l	65472	3.7%
Other values (85)	551251	31.0%

None

Value	Count	Frequency (%)
å	3015	38.5%
ø	2735	34.9%
æ	1555	19.9%
´	123	1.6%
é	118	1.5%
Ø	107	1.4%
	16	0.2%
á	13	0.2%
ü	11	0.1%
ñ	11	0.1%
Other values (42)	125	1.6%

Punctuation

Value	Count	Frequency (%)
’	846	75.1%
–	60	5.3%
”	55	4.9%
“	50	4.4%
‍	49	4.3%
—	36	3.2%
•	15	1.3%
…	9	0.8%
‘	7	0.6%

VS

Value	Count	Frequency (%)
️	11	100.0%

Hiragana

Value	Count	Frequency (%)
い	7	9.2%
の	6	7.9%
に	6	7.9%
し	6	7.9%
ん	5	6.6%
で	3	3.9%
は	3	3.9%
ま	3	3.9%
す	3	3.9%
さ	3	3.9%
Other values (20)	31	40.8%

Dingbats

Value	Count	Frequency (%)
✨	6	50.0%
❤	4	33.3%
✌	2	16.7%

Misc Symbols

Value	Count	Frequency (%)
☺	5	100.0%

CJK

Value	Count	Frequency (%)
日	3	6.0%
本	3	6.0%
住	3	6.0%
安	2	4.0%
皆	2	4.0%
在	2	4.0%
学	2	4.0%
中	2	4.0%
疲	1	2.0%
旅	1	2.0%
Other values (29)	29	58.0%

Hangul

Value	Count	Frequency (%)
요	2	13.3%
어	2	13.3%
세	1	6.7%
금	1	6.7%
왔	1	6.7%
배	1	6.7%
조	1	6.7%
가	1	6.7%
극	1	6.7%
한	1	6.7%
Other values (3)	3	20.0%

host_response_time
Categorical

HIGH CORRELATION MISSING

Distinct	4
Distinct (%)	< 0.1%
Missing	3753
Missing (%)	27.2%
Memory size	215.9 KiB

within an hour	4835
within a day	2469
within a few hours	2281
a few days or more	477

Length

Max length	18
Median length	14
Mean length	14.605645
Min length	12

Characters and Unicode

Total characters	146962
Distinct characters	16
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	within a few hours
2nd row	within a few hours
3rd row	within an hour
4th row	within a few hours
5th row	within a day

Common Values

Value	Count	Frequency (%)
within an hour	4835	35.0%
within a day	2469	17.9%
within a few hours	2281	16.5%
a few days or more	477	3.5%
(Missing)	3753	27.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
within	9585	28.7%
a	5227	15.6%
an	4835	14.5%
hour	4835	14.5%
few	2758	8.3%
day	2469	7.4%
hours	2281	6.8%
days	477	1.4%
or	477	1.4%
more	477	1.4%

Most occurring characters

Value	Count	Frequency (%)
	23359	15.9%
i	19170	13.0%
h	16701	11.4%
n	14420	9.8%
a	13008	8.9%
w	12343	8.4%
t	9585	6.5%
o	8070	5.5%
r	8070	5.5%
u	7116	4.8%
Other values (6)	15120	10.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	123603	84.1%
Space Separator	23359	15.9%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
i	19170	15.5%
h	16701	13.5%
n	14420	11.7%
a	13008	10.5%
w	12343	10.0%
t	9585	7.8%
o	8070	6.5%
r	8070	6.5%
u	7116	5.8%
e	3235	2.6%
Other values (5)	11885	9.6%

Space Separator

Value	Count	Frequency (%)
	23359	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	123603	84.1%
Common	23359	15.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
i	19170	15.5%
h	16701	13.5%
n	14420	11.7%
a	13008	10.5%
w	12343	10.0%
t	9585	7.8%
o	8070	6.5%
r	8070	6.5%
u	7116	5.8%
e	3235	2.6%
Other values (5)	11885	9.6%

Common

Value	Count	Frequency (%)
	23359	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	146962	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	23359	15.9%
i	19170	13.0%
h	16701	11.4%
n	14420	9.8%
a	13008	8.9%
w	12343	8.4%
t	9585	6.5%
o	8070	5.5%
r	8070	5.5%
u	7116	4.8%
Other values (6)	15120	10.3%

host_response_rate
Categorical

HIGH CARDINALITY HIGH CORRELATION IMBALANCE MISSING

Distinct	63
Distinct (%)	0.6%
Missing	3753
Missing (%)	27.2%
Memory size	215.9 KiB

100%	7664
90%	315
0%	260
80%	219
50%	209
Other values (58)	1395

Length

Max length	4
Median length	4
Mean length	3.7358378
Min length	2

Characters and Unicode

Total characters	37590
Distinct characters	11
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	9 ?
Unique (%)	0.1%

Sample

1st row	100%
2nd row	100%
3rd row	100%
4th row	100%
5th row	100%

Common Values

Value	Count	Frequency (%)
100%	7664	55.5%
90%	315	2.3%
0%	260	1.9%
80%	219	1.6%
50%	209	1.5%
99%	195	1.4%
60%	161	1.2%
75%	125	0.9%
67%	117	0.8%
70%	79	0.6%
Other values (53)	718	5.2%
(Missing)	3753	27.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
100	7664	76.2%
90	315	3.1%
0	260	2.6%
80	219	2.2%
50	209	2.1%
99	195	1.9%
60	161	1.6%
75	125	1.2%
67	117	1.2%
70	79	0.8%
Other values (53)	718	7.1%

Most occurring characters

Value	Count	Frequency (%)
0	16665	44.3%
%	10062	26.8%
1	7737	20.6%
9	910	2.4%
8	540	1.4%
7	430	1.1%
5	391	1.0%
6	383	1.0%
3	230	0.6%
2	142	0.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	27528	73.2%
Other Punctuation	10062	26.8%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	16665	60.5%
1	7737	28.1%
9	910	3.3%
8	540	2.0%
7	430	1.6%
5	391	1.4%
6	383	1.4%
3	230	0.8%
2	142	0.5%
4	100	0.4%

Other Punctuation

Value	Count	Frequency (%)
%	10062	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	37590	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	16665	44.3%
%	10062	26.8%
1	7737	20.6%
9	910	2.4%
8	540	1.4%
7	430	1.1%
5	391	1.0%
6	383	1.0%
3	230	0.6%
2	142	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	37590	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	16665	44.3%
%	10062	26.8%
1	7737	20.6%
9	910	2.4%
8	540	1.4%
7	430	1.1%
5	391	1.0%
6	383	1.0%
3	230	0.6%
2	142	0.4%

Auto

Heatmap
Table

	host_id	latitude	longitude	price	minimum_nights	number_of_reviews	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	accommodates	review_scores_value	review_scores_cleanliness	review_scores_location	review_scores_accuracy	review_scores_communication	review_scores_checkin	review_scores_rating	maximum_nights	neighbourhood	room_type	property_type	host_is_superhost	host_response_time	host_response_rate
host_id	1.000	-0.009	0.014	-0.028	-0.177	-0.204	0.213	0.100	0.066	0.029	-0.096	-0.042	-0.025	0.006	-0.005	-0.021	-0.012	-0.033	-0.035	0.036	0.040	0.149	0.124	0.072	0.201
latitude	-0.009	1.000	-0.216	-0.061	0.040	-0.016	-0.034	-0.071	0.018	-0.025	-0.058	-0.022	-0.025	-0.041	-0.013	-0.000	0.007	-0.010	-0.022	0.478	0.057	0.153	0.066	0.046	0.070
longitude	0.014	-0.216	1.000	0.181	-0.026	0.077	0.046	0.068	0.037	0.070	0.013	-0.044	-0.019	0.129	-0.022	-0.043	-0.037	-0.026	0.029	0.554	0.073	0.183	0.047	0.037	0.057
price	-0.028	-0.061	0.181	1.000	0.013	-0.103	-0.006	0.017	0.209	-0.001	0.522	0.014	0.076	0.234	0.076	0.080	0.083	0.116	0.046	0.024	0.000	0.000	0.017	0.042	0.250
minimum_nights	-0.177	0.040	-0.026	0.013	1.000	-0.064	-0.298	-0.175	-0.054	-0.184	0.101	0.097	0.039	0.047	0.078	0.114	0.100	0.095	0.004	0.000	0.000	0.000	0.000	0.003	0.000
number_of_reviews	-0.204	-0.016	0.077	-0.103	-0.064	1.000	0.241	0.011	-0.035	0.604	-0.001	-0.239	-0.202	-0.304	-0.341	-0.406	-0.368	-0.317	0.096	0.034	0.130	0.139	0.234	0.059	0.051
reviews_per_month	0.213	-0.034	0.046	-0.006	-0.298	0.241	1.000	0.061	0.279	0.645	-0.117	-0.060	-0.041	-0.039	-0.078	-0.141	-0.121	-0.052	-0.102	0.036	0.164	0.235	0.084	0.099	0.000
calculated_host_listings_count	0.100	-0.071	0.068	0.017	-0.175	0.011	0.061	1.000	0.097	0.031	-0.002	-0.156	-0.074	-0.041	-0.139	-0.172	-0.114	-0.155	0.156	0.090	0.030	0.393	0.330	0.115	0.728
availability_365	0.066	0.018	0.037	0.209	-0.054	-0.035	0.279	0.097	1.000	0.142	-0.016	-0.101	-0.044	0.004	-0.082	-0.104	-0.043	-0.072	-0.024	0.047	0.065	0.090	0.021	0.097	0.111
number_of_reviews_ltm	0.029	-0.025	0.070	-0.001	-0.184	0.604	0.645	0.031	0.142	1.000	-0.032	-0.128	-0.119	-0.093	-0.172	-0.225	-0.215	-0.130	-0.003	0.039	0.172	0.244	0.157	0.052	0.028
accommodates	-0.096	-0.058	0.013	0.522	0.101	-0.001	-0.117	-0.002	-0.016	-0.032	1.000	0.002	-0.032	0.019	-0.017	0.020	0.034	0.004	0.095	0.069	0.284	0.230	0.043	0.018	0.080
review_scores_value	-0.042	-0.022	-0.044	0.014	0.097	-0.239	-0.060	-0.156	-0.101	-0.128	0.002	1.000	0.498	0.370	0.551	0.435	0.419	0.643	-0.075	0.027	0.025	0.067	0.085	0.037	0.086
review_scores_cleanliness	-0.025	-0.025	-0.019	0.076	0.039	-0.202	-0.041	-0.074	-0.044	-0.119	-0.032	0.498	1.000	0.264	0.531	0.377	0.368	0.613	-0.067	0.024	0.001	0.007	0.101	0.041	0.117
review_scores_location	0.006	-0.041	0.129	0.234	0.047	-0.304	-0.039	-0.041	0.004	-0.093	0.019	0.370	0.264	1.000	0.336	0.329	0.292	0.383	-0.032	0.107	0.040	0.072	0.051	0.024	0.096
review_scores_accuracy	-0.005	-0.013	-0.022	0.076	0.078	-0.341	-0.078	-0.139	-0.082	-0.172	-0.017	0.551	0.531	0.336	1.000	0.530	0.504	0.651	-0.078	0.016	0.085	0.105	0.063	0.048	0.092
review_scores_communication	-0.021	-0.000	-0.043	0.080	0.114	-0.406	-0.141	-0.172	-0.104	-0.225	0.020	0.435	0.377	0.329	0.530	1.000	0.569	0.536	-0.080	0.025	0.040	0.075	0.047	0.047	0.150
review_scores_checkin	-0.012	0.007	-0.037	0.083	0.100	-0.368	-0.121	-0.114	-0.043	-0.215	0.034	0.419	0.368	0.292	0.504	0.569	1.000	0.477	-0.079	0.005	0.047	0.080	0.048	0.024	0.138
review_scores_rating	-0.033	-0.010	-0.026	0.116	0.095	-0.317	-0.052	-0.155	-0.072	-0.130	0.004	0.643	0.613	0.383	0.651	0.536	0.477	1.000	-0.080	0.024	0.041	0.059	0.078	0.059	0.117
maximum_nights	-0.035	-0.022	0.029	0.046	0.004	0.096	-0.102	0.156	-0.024	-0.003	0.095	-0.075	-0.067	-0.032	-0.078	-0.080	-0.079	-0.080	1.000	0.035	0.013	0.144	0.106	0.034	0.105
neighbourhood	0.036	0.478	0.554	0.024	0.000	0.034	0.036	0.090	0.047	0.039	0.069	0.027	0.024	0.107	0.016	0.025	0.005	0.024	0.035	1.000	0.075	0.164	0.083	0.061	0.072
room_type	0.040	0.057	0.073	0.000	0.000	0.130	0.164	0.030	0.065	0.172	0.284	0.025	0.001	0.040	0.085	0.040	0.047	0.041	0.013	0.075	1.000	0.961	0.105	0.052	0.199
property_type	0.149	0.153	0.183	0.000	0.000	0.139	0.235	0.393	0.090	0.244	0.230	0.067	0.007	0.072	0.105	0.075	0.080	0.059	0.144	0.164	0.961	1.000	0.290	0.133	0.114
host_is_superhost	0.124	0.066	0.047	0.017	0.000	0.234	0.084	0.330	0.021	0.157	0.043	0.085	0.101	0.051	0.063	0.047	0.048	0.078	0.106	0.083	0.105	0.290	1.000	0.172	0.350
host_response_time	0.072	0.046	0.037	0.042	0.003	0.059	0.099	0.115	0.097	0.052	0.018	0.037	0.041	0.024	0.048	0.047	0.024	0.059	0.034	0.061	0.052	0.133	0.172	1.000	0.629
host_response_rate	0.201	0.070	0.057	0.250	0.000	0.051	0.000	0.728	0.111	0.028	0.080	0.086	0.117	0.096	0.092	0.150	0.138	0.117	0.105	0.072	0.199	0.114	0.350	0.629	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	minimum_nights	number_of_reviews	last_review	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license	property_type	accommodates	first_review	review_scores_value	review_scores_cleanliness	review_scores_location	review_scores_accuracy	review_scores_communication	review_scores_checkin	review_scores_rating	maximum_nights	listing_url	host_is_superhost	host_about	host_response_time	host_response_rate
id
6983	Copenhagen 'N Livin'	16774	Simon	NaN	Nrrebro	55.686410	12.547410	Entire home/apt	898	3	172	2022-06-21	1.08	1	0	4	NaN	Entire rental unit	2	2009-09-04	4.71	4.78	4.73	4.79	4.89	4.86	4.78	15	https://www.airbnb.com/rooms/6983	f	I'm currently working as an environmental consultant for a large engineering consultancy in Copenhagen.\r\nWhen I'm not at work, I spend time doing sports (playing football, running, cross fit), or doing indoor activities such as reading books and listening to music. I have recently taken an interest in cooking, and I love great food. \r\nI'm outgoing, happy and love good company.\r\nAnd I love my bike as any other person from Copenhagen..	within a few hours	100%
26057	Lovely house - most attractive area	109777	Kari	NaN	Indre By	55.693070	12.576490	Entire home/apt	2600	4	59	2022-08-09	0.55	1	303	8	NaN	Entire home	6	2013-12-02	4.81	4.96	4.94	4.93	4.86	4.93	4.91	1125	https://www.airbnb.com/rooms/26057	f	We are a family with 2 children, and living in a great and beautiful house placed in a very special part in central Copenhagen called Kartoffelraekkerne. It's like a small village in the center of the city! The house is available from time to time - when we are travelling/working abroad. We're looking forward to hearing from you.	within a few hours	100%
26473	City Centre Townhouse Sleeps 1-10 persons	112210	Julia	NaN	Indre By	55.676020	12.575400	Entire home/apt	3250	3	300	2022-09-10	2.06	3	56	7	NaN	Entire townhouse	11	2010-10-14	4.59	4.44	4.89	4.63	4.70	4.78	4.53	31	https://www.airbnb.com/rooms/26473	f	Active young woman, started as an expat in Copenhagen and settled it as a new home. During this process, I have learnt a lot about the art of housing, and made it my daily activity. While I prefer to let you conquer the city on your own paths, please feel free to use my experience if you need any tips & advice.\n\nWill do my best to give you a perfect slice of Copenhagen.\n\nIn my spare time I love consuming good food, taking long bike rides or discovering new music.\n\nI live & work here and can be contacted with any comments & questions.	within an hour	100%
29118	Best Location in Cool Istedgade	125230	Nana	NaN	Vesterbro-Kongens Enghave	55.670230	12.555040	Entire home/apt	725	7	24	2022-08-04	0.16	1	59	2	NaN	Entire rental unit	2	2010-06-17	4.80	4.73	4.87	4.87	5.00	5.00	4.92	14	https://www.airbnb.com/rooms/29118	f	I have a Master of Arts in Musicology and I work as a vocal coach and a singer and I LOVE to travel and meet people from all over the world. \nI live in lively Vesterbro with my 5 yr. old son Wili.	within a few hours	100%
31094	Beautiful, spacious, central, renovated Penthouse	129976	Ebbe	NaN	Vesterbro-Kongens Enghave	55.666602	12.555283	Entire home/apt	1954	3	19	2022-08-22	0.13	1	0	2	NaN	Entire condo	6	2010-08-16	4.53	4.88	4.80	4.82	4.82	4.87	4.88	10	https://www.airbnb.com/rooms/31094	f	Hi and welcome. My name is Ebbe, I am a medical doctor working in Copenhagen. I live in the flat with my girlfriend Lea who is working as a nurse. We have two little girls: Nora is 6 years old and Luna is 2 years old.\n\nWe love sports, music and travelling, and we look forward welcoming you to Wonderful Copenhagen :-)	within a day	100%
32379	165 m2 artist flat on Vesterbro, with 2 bathrooms	140105	Lise	NaN	Vesterbro-Kongens Enghave	55.672638	12.552493	Entire home/apt	1280	3	80	2022-08-13	0.54	2	60	5	NaN	Entire rental unit	4	2010-08-23	4.72	4.92	4.88	4.96	4.91	4.88	4.90	365	https://www.airbnb.com/rooms/32379	t	As profession - Set and Costumedesigner for Avangarde Theatre, Modern Dance and Performance. A traveler, enjoy to se the whole world, meet people and talk about the world situation	within a few hours	100%
32841	Cozy flat for Adults/Quiet for kids	142143	Anders & Maria	NaN	sterbro	55.711760	12.570910	Entire home/apt	617	100	7	2016-09-15	0.05	1	281	0	NaN	Entire rental unit	4	2010-07-25	4.50	4.50	4.50	4.75	5.00	5.00	4.57	1125	https://www.airbnb.com/rooms/32841	f	Anders:\r\nHitchhiked 100.000 km's, Been publicly speaking more than 500 times, Traveled 80 countries, Lived 36 yrs. Have 7 sisters and brothers, Twins, 1 wife... and a million plans!\r\nMaria:\r\nSinger/nurse/Twin-Mum/training to be a midwife. Lived 6 years in London and toured the world with a British Pop band…including an appearance at the Bollywood Awards:-)	NaN	NaN
33680	Best location on Vesterbro/Cph	145671	Mette	NaN	Vesterbro-Kongens Enghave	55.666310	12.545550	Entire home/apt	1000	6	71	2019-03-14	0.48	1	312	0	NaN	Entire rental unit	4	2010-07-26	4.67	4.67	4.80	4.75	4.91	4.91	4.74	60	https://www.airbnb.com/rooms/33680	f	Danish Artist living in Copenhagen. I travel a lot and enjoy using airbnb both for my own flat but also to rent places. My area is great and I always enjoy to be here when Im home. I will be very happy to advise you on where to go and what to do when you are here. \r\n	NaN	NaN
37159	Unique space on greatest location	160390	Jeanette	NaN	Indre By	55.685470	12.565430	Entire home/apt	2916	5	11	2017-08-22	0.07	1	0	0	NaN	Entire rental unit	4	2010-08-11	4.78	5.00	4.89	4.78	5.00	5.00	5.00	21	https://www.airbnb.com/rooms/37159	f	Hi my name is Jeanette and I live and work with my husband Casper in Copenhagen with something called Behavioral Architecture. Together we have 2 kids Elvis on 16 and Vivelill is 12. When we travel we love to go as locale as possible, to experience the nuances in the different cultures we visit :)	NaN	NaN
38499	0 min. from everything in Cph.	122489	Christina	NaN	Indre By	55.684288	12.573019	Entire home/apt	1900	14	15	2022-09-17	0.10	1	87	6	NaN	Entire rental unit	5	2010-08-21	4.89	4.89	5.00	4.56	5.00	5.00	5.00	70	https://www.airbnb.com/rooms/38499	f	We are...\nCarsten and Christina, and our awesome 3 kids. Carsten is a freelance photographer and Christina works in the NGO sector concerning refugees globally. Traveling is a passion we share, and airbnb has added greatly to the fantastic experiences we have had from the Vancouver skyline flat to the riad in Old Marrakech. \nWe love our city and enjoy going out or just hanging out people watching over the rim of a well brewed cup of latte in the parks around our place. \nYou are...\n...welcome to stay in our home whoever you are. As long as you take good care of it. We love our place, and we hope you will too.	NaN	NaN

	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	minimum_nights	number_of_reviews	last_review	reviews_per_month	calculated_host_listings_count	availability_365	number_of_reviews_ltm	license	property_type	accommodates	first_review	review_scores_value	review_scores_cleanliness	review_scores_location	review_scores_accuracy	review_scores_communication	review_scores_checkin	review_scores_rating	maximum_nights	listing_url	host_is_superhost	host_about	host_response_time	host_response_rate
id
638839422729041798	T31B, 1. A 1 bedroom apartment in Rødovre	424093643	Seneca	NaN	Vanlse	55.672860	12.456100	Entire home/apt	490	4	6	2022-08-21	2.17	26	0	6	NaN	Entire serviced apartment	2	2022-07-05	3.83	4.00	3.67	4.17	4.17	4.33	4.17	365	https://www.airbnb.com/rooms/638839422729041798	f	Seneca Service Company has a variety of furnished services apartments located in Central Copenhagen.	within an hour	100%
630779770351807878	Skøn lejlighed i Hvidovre	60240725	Henrik	NaN	Valby	55.638700	12.498240	Entire home/apt	1000	6	0	NaN	NaN	1	0	0	NaN	Entire condo	3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1125	https://www.airbnb.com/rooms/630779770351807878	f	NaN	NaN	NaN
632474141549608786	3 Room apartment. 8 min walk from CPH Airport	76731355	Jørgen	NaN	Amager st	55.630900	12.642480	Entire home/apt	845	1	8	2022-09-11	2.11	1	0	8	NaN	Entire condo	4	2022-06-04	4.88	4.75	4.88	5.00	5.00	5.00	4.88	1125	https://www.airbnb.com/rooms/632474141549608786	f	NaN	within an hour	100%
646726550705810749	Ny rummelig lejlighed med tilhørende kat.	54229471	Morten	NaN	Brnshj-Husum	55.739028	12.487433	Entire home/apt	856	6	4	2022-08-28	1.85	1	0	4	NaN	Entire rental unit	5	2022-07-23	5.00	4.00	4.50	4.75	5.00	5.00	4.75	7	https://www.airbnb.com/rooms/646726550705810749	f	NaN	within an hour	100%
646941499450912133	Big beautiful and charming apartment	141288846	Tanja	NaN	Bispebjerg	55.730481	12.521243	Entire home/apt	1050	2	4	2022-09-04	1.64	2	0	4	NaN	Entire rental unit	3	2022-07-15	5.00	5.00	4.75	4.50	5.00	5.00	5.00	30	https://www.airbnb.com/rooms/646941499450912133	f	NaN	within an hour	100%
653494030951422457	Top-floor Villa Apartment in the Heart of Hellerup	465189427	Martine	NaN	sterbro	55.732840	12.572370	Entire home/apt	1250	14	0	NaN	NaN	1	0	0	NaN	Entire condo	4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	50	https://www.airbnb.com/rooms/653494030951422457	f	NaN	NaN	NaN
647809303952891559	Dejligt lille hus med flere hyggekroge ude og inde	4862421	Kathrine	NaN	Valby	55.667782	12.463285	Entire home/apt	450	3	3	2022-07-21	1.01	1	0	3	NaN	Entire home	4	2022-06-29	5.00	5.00	4.67	5.00	5.00	5.00	5.00	1125	https://www.airbnb.com/rooms/647809303952891559	f	I´m 39, living in Rødovre, Copenhagen with my son who is 6 years old. I work with recruiting and spend most of my free time with my son enjoying time with neighbours and friends in the garden or the nice area with the big lake, Damhussøen. I also love spending time inside being cozy with a cup of tea and cooking to my friends and family.	within a day	100%
650819220455514228	Lejlighed i Storkøbenhavn. 13 minutter fra centrum	134647873	Tim	NaN	Valby	55.659536	12.474726	Entire home/apt	400	1	3	2022-08-14	1.27	1	0	3	NaN	Entire condo	3	2022-07-17	5.00	4.67	4.67	5.00	5.00	5.00	5.00	365	https://www.airbnb.com/rooms/650819220455514228	f	NaN	within an hour	100%
658612163688161695	Cosy apartment with a great view in Copenhagen	256903668	Sam	NaN	Brnshj-Husum	55.730971	12.487993	Entire home/apt	850	1	0	NaN	NaN	1	0	0	NaN	Entire rental unit	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	365	https://www.airbnb.com/rooms/658612163688161695	f	NaN	within a few hours	100%
648436253362373119	Big Bedroom connected with a large living room	141288846	Tanja	NaN	Bispebjerg	55.731420	12.521770	Private room	420	2	1	2022-07-07	0.37	2	0	1	NaN	Private room in rental unit	2	2022-07-07	5.00	5.00	5.00	5.00	5.00	5.00	5.00	30	https://www.airbnb.com/rooms/648436253362373119	f	NaN	within an hour	100%

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Other Symbol

Lowercase Letter

Uppercase Letter

Other Letter

Other Punctuation

Decimal Number

Math Symbol

Modifier Symbol

Dash Punctuation

Close Punctuation

Final Punctuation

Initial Punctuation

Format

Space Separator

Open Punctuation

Control

Nonspacing Mark

Other Number

Connector Punctuation

Currency Symbol

Most occurring scripts

Most frequent character per script

Common

Latin

Han

Hiragana

Inherited

Most occurring blocks

Most frequent character per block

ASCII

None

Misc Symbols

Punctuation

VS

Dingbats

CJK

Emoticons

Enclosed Alphanum Sup

Hiragana

Enclosed Alphanum

Currency Symbols

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Decimal Number

Other Punctuation

Space Separator

Dash Punctuation

Close Punctuation

Open Punctuation

Math Symbol

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

None

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Dash Punctuation

Most occurring scripts